{"as_of":"2026-08-19T15:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ccdd97e890560f9746cbae97955edf9f3b8fcdb122d3e559487cf7056dd0006f","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T23:34:11.289646Z","state":"measured"},{"denominator":135,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":135,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":57,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:59:53.054525Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2504.14135","last_updated":"2026-04-07T08:59:35Z","snapshot_observed_at":"2026-08-15T08:57:35.829790Z","submitted_at":"2025-04-19T01:54:45Z","title":"Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-22T18:50:57.738602Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2504.14135"},"observation_digest":"sha256:f6baa74b08251c7b77d4e3608c189da3c832da49126783b63aa3b8fce43f213c","observation_id":"4c1ade24-0ab4-4d65-9264-0ef667cb2976","resolution":{"observed_at":"2026-05-22T18:51:57.332577Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-16T10:59:53.054525Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.16738","last_updated":"2026-07-06T15:23:50Z","snapshot_observed_at":"2026-08-18T11:06:16.961649Z","submitted_at":"2025-04-23T14:09:42Z","title":"MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T10:59:53.054525Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2504.16738"},"observation_digest":"sha256:1194fa2c4258f4c4ee5181b8027fb6a7070d2f0599f1122a5927e4dadd5665ed","observation_id":"aa772c30-5458-4448-8183-e2a2046b1e05","resolution":{"observed_at":"2026-08-16T10:59:53.054525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-15T23:48:57.968507Z","title":"Zakka, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.03729","last_updated":"2025-08-29T05:22:52Z","snapshot_observed_at":"2026-08-15T23:41:33.590964Z","submitted_at":"2025-05-06T17:57:12Z","title":"Visual Imitation Enables Contextual Humanoid Control","version":5},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T23:48:57.968507Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2505.03729"},"observation_digest":"sha256:7d3ec69afcd9bfb1c14bee4ac09d38a6cc17bdd2823e1677215dfe2d9ed48cc4","observation_id":"b164d273-89f8-49aa-92c1-55c0e14cf947","resolution":{"observed_at":"2026-08-15T23:48:57.968507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-15T21:45:35.064770Z","title":"”MuJoCo Playground.” arXiv preprint arXiv:2502.08844 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.09029","last_updated":"2025-05-13T23:56:12Z","snapshot_observed_at":"2026-08-18T03:51:37.643313Z","submitted_at":"2025-05-13T23:56:12Z","title":"Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:45:35.064770Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2505.09029"},"observation_digest":"sha256:f7522ec1b6b3195e991b2ddeb7d05d8a38bb7131ac9bddaf09129fabafe01919","observation_id":"0184d00b-5bf5-4fab-ac08-c62caccdaf01","resolution":{"observed_at":"2026-08-15T21:45:35.064770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-15T20:57:31.382654Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11494","last_updated":"2026-06-22T08:26:09Z","snapshot_observed_at":"2026-08-18T20:41:05.078813Z","submitted_at":"2025-05-16T17:57:03Z","title":"SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:57:31.382654Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2505.11494"},"observation_digest":"sha256:93dde404718f74e324c95157d2f151669fab837f83b4a910a5daa326248a6879","observation_id":"61157ede-b9ad-44e5-b8be-61cee418ef4f","resolution":{"observed_at":"2026-08-15T20:57:31.382654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-07T13:08:35.735651Z","title":"Mujoco playground","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.22642","last_updated":"2025-06-01T22:51:56Z","snapshot_observed_at":"2026-08-16T09:27:51.053286Z","submitted_at":"2025-05-28T17:55:26Z","title":"FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:08:35.735651Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2505.22642"},"observation_digest":"sha256:98d1da8896130044a397db093340fb1f1ec5a65d664ce4996a6fa2a28e55467f","observation_id":"2591ff17-1047-4344-9c37-9a96b3937f3d","resolution":{"observed_at":"2026-08-07T13:08:35.735651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-15T19:45:00.972627Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.15132","last_updated":"2025-06-18T04:24:49Z","snapshot_observed_at":"2026-08-15T19:40:23.149092Z","submitted_at":"2025-06-18T04:24:49Z","title":"Booster Gym: An End-to-End Reinforcement Learning Framework for Humanoid Robot Locomotion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T19:45:00.972627Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2506.15132"},"observation_digest":"sha256:1dd49b7d4ff4046230ed5a8a1373630ad5f8e376059b9a947e4aa6e6d9029f19","observation_id":"45f4eb52-46d9-4505-93a4-632df8cc7b34","resolution":{"observed_at":"2026-08-15T19:45:00.972627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-06T19:53:06.275060Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.04452","last_updated":"2025-07-06T16:18:40Z","snapshot_observed_at":"2026-08-17T08:27:08.862765Z","submitted_at":"2025-07-06T16:18:40Z","title":"SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:06.275060Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2507.04452"},"observation_digest":"sha256:73bfd9c218383cc96e7b7169db93faa2019a61b936dc6dfe643d7235a7fef9d7","observation_id":"0e5d95f8-c6c6-4f32-9034-9d550f6f1e88","resolution":{"observed_at":"2026-08-06T19:53:06.275060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-06T13:07:09.516529Z","title":"Mujoco playground","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.21053","last_updated":"2025-08-01T13:04:28Z","snapshot_observed_at":"2026-08-18T23:52:36.906166Z","submitted_at":"2025-07-28T17:59:57Z","title":"Flow Matching Policy Gradients","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T13:07:09.516529Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2507.21053"},"observation_digest":"sha256:49ab8c0b334b54ef9eefb4e7e15d7b2385777676948fce29e7d58db7488c3993","observation_id":"91c46414-c837-4d29-8744-b3125bb80fa6","resolution":{"observed_at":"2026-08-06T13:07:09.516529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-06T11:13:12.409367Z","title":"Mujoco playground","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22885","last_updated":"2025-07-30T17:59:31Z","snapshot_observed_at":"2026-08-06T11:13:05.075910Z","submitted_at":"2025-07-30T17:59:31Z","title":"Viser: Imperative, Web-based 3D Visualization in Python","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T11:13:12.409367Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2507.22885"},"observation_digest":"sha256:6de53986b164895d540922ca31590d09b9e98dcfa7b3d71b4e125420bd10bd21","observation_id":"ae6cdf95-ba5c-4a97-9682-e5ab0e3b39c9","resolution":{"observed_at":"2026-08-06T11:13:12.409367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-15T17:21:25.270760Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.12928","last_updated":"2025-08-18T13:53:38Z","snapshot_observed_at":"2026-08-19T09:41:00.011603Z","submitted_at":"2025-08-18T13:53:38Z","title":"Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T17:21:25.270760Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2508.12928"},"observation_digest":"sha256:63af27bfdb5171258069e0b322da2a14fd7c4f0dd12579d6a3f5d41012215839","observation_id":"1915220b-0f3f-4a41-b915-29c425c5bd3e","resolution":{"observed_at":"2026-08-15T17:21:25.270760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T16:55:52.309377Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.17449","last_updated":"2025-09-04T16:46:34Z","snapshot_observed_at":"2026-08-19T06:18:36.723707Z","submitted_at":"2025-08-24T17:01:15Z","title":"Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges","version":2},"reference_index":114,"source":"pdf_text","source_observed_at":"2026-08-05T16:55:52.309377Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2508.17449"},"observation_digest":"sha256:4aeb0cfab42dd683cea1b93499ef497b7fcc2473b6aa167aed69af5e3040548d","observation_id":"7717e797-ead4-4540-a0a2-a07d8add70f1","resolution":{"observed_at":"2026-08-05T16:55:52.309377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-04T17:56:38.873029Z","title":"Kahrs, Carmelo Sferrazza, Yuval Tassa, and Pieter Abbeel","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10397","last_updated":"2026-07-25T20:09:07Z","snapshot_observed_at":"2026-08-19T04:49:00.708675Z","submitted_at":"2025-09-12T16:44:34Z","title":"RecoWorld: Building Simulated Environments for Agentic Recommender Systems","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-04T17:56:38.873029Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2509.10397"},"observation_digest":"sha256:04be5bfe3d5367ea4f46031e4dd16a27e0ed4d1be0d55d6bdf395bc59558343b","observation_id":"6f27ce27-967c-4cec-812d-5bf4fb298b3c","resolution":{"observed_at":"2026-08-04T17:56:38.873029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-02T22:58:38.900601Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.15245","last_updated":"2026-06-23T08:31:42Z","snapshot_observed_at":"2026-08-16T10:42:09.922672Z","submitted_at":"2026-02-16T22:51:57Z","title":"MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-02T22:58:38.900601Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2602.15245"},"observation_digest":"sha256:f266079f0bc69ba83051dcf5526bfcd8a3129ce862e9dc4ef04882c4d6b919f9","observation_id":"3ae413aa-1f6b-4178-af74-66361ca0ea93","resolution":{"observed_at":"2026-08-02T22:58:38.900601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-02T21:36:15.812654Z","title":"Mujoco playground.arXiv preprint arXiv:2502.08844, 2025.(Cited on pages 1, 4, 7, 8, 16, 18, and 19)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.20220","last_updated":"2026-07-22T18:26:39Z","snapshot_observed_at":"2026-08-15T05:21:43.801650Z","submitted_at":"2026-02-23T10:34:15Z","title":"What Matters for Simulation to Online Reinforcement Learning on Real Robots","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T21:36:15.812654Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2602.20220"},"observation_digest":"sha256:958168a32e8e6a5a29c33f964ad706ae494db587ce8f7b0b2da4f942ef16d458","observation_id":"e0fe0836-7a91-45e0-b36e-41a57adab712","resolution":{"observed_at":"2026-08-02T21:36:15.812654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2603.04531","last_updated":"2026-06-18T16:44:12Z","snapshot_observed_at":"2026-08-17T14:32:15.209104Z","submitted_at":"2026-03-04T19:17:42Z","title":"PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T16:35:34.179788Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2603.04531"},"observation_digest":"sha256:3deff4bc79e9012bde8490d7b4d2e3727203295129be5bbacda2553b2f955ac1","observation_id":"4aaa48ef-34b2-4e8b-beba-ff37f25dc449","resolution":{"observed_at":"2026-05-15T16:36:17.295232Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-02T18:52:42.535173Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.04531","last_updated":"2026-06-18T16:44:12Z","snapshot_observed_at":"2026-08-17T14:32:15.209104Z","submitted_at":"2026-03-04T19:17:42Z","title":"PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T18:52:42.535173Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2603.04531"},"observation_digest":"sha256:0c1c82937e103e607e75dc793047ac7731faf8685040d5d8f692879743041848","observation_id":"f5fd9519-96ff-4027-9d3f-7769c260314c","resolution":{"observed_at":"2026-08-02T18:52:42.535173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2603.12612","last_updated":"2026-05-04T08:54:07Z","snapshot_observed_at":"2026-07-06T22:48:53.198077Z","submitted_at":"2026-03-13T03:27:25Z","title":"FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T12:17:59.392158Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2603.12612"},"observation_digest":"sha256:107d7cf7b159fa3769169205bdd9b2b9dea3fecd214b15d367d9d3353c3dfdf8","observation_id":"febfeef0-bb3b-4482-8c81-822975be3825","resolution":{"observed_at":"2026-05-15T12:20:00.675540Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-07-15T11:52:28.028968Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.22510","last_updated":"2026-07-15T01:44:53Z","snapshot_observed_at":"2026-08-18T09:14:45.703701Z","submitted_at":"2026-03-23T19:16:54Z","title":"Research Novelty in Information Systems Journals After ChatGPT: Differences Across Institutional Language Contexts","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-15T11:52:28.028968Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2603.22510"},"observation_digest":"sha256:7d634f69ea1f0f1fdcaafc3208f50d46b4054ced9a4c50c356399681a7ef320c","observation_id":"04a3f884-5279-4807-b3ed-50095a520332","resolution":{"observed_at":"2026-07-15T11:52:28.028968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.04138","last_updated":"2026-06-30T14:47:48Z","snapshot_observed_at":"2026-08-14T22:04:08.330018Z","submitted_at":"2026-04-05T14:53:43Z","title":"Learning Dexterous Grasping from Sparse Taxonomy Guidance","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-13T17:05:06.300013Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.04138"},"observation_digest":"sha256:06e2d9e7632d667b95a3cfde87418e961997bfea95d3cd74d472796224a4c0c0","observation_id":"be7679be-271e-4c71-9b7d-e472d73b5518","resolution":{"observed_at":"2026-05-13T17:08:00.984225Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-07-13T11:14:37.619203Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.04141","last_updated":"2026-06-17T17:55:46Z","snapshot_observed_at":"2026-08-12T12:42:58.840826Z","submitted_at":"2026-04-05T14:59:47Z","title":"On Data Thinning for Model Validation in Small Area Estimation","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-13T11:14:37.619203Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.04141"},"observation_digest":"sha256:0910e0b6f3a0b3e59e9620f7cf0f33e1991788ceb6bc8cd4d082e6b3f3e1728f","observation_id":"891402cb-515a-4e4d-8899-39c91b272998","resolution":{"observed_at":"2026-07-13T11:14:37.619203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.04539","last_updated":"2026-05-15T07:15:36Z","snapshot_observed_at":"2026-08-14T16:29:37.712652Z","submitted_at":"2026-04-06T09:03:41Z","title":"FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-10T20:04:56.512544Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.04539"},"observation_digest":"sha256:cd33c345d708e6efb9a5807bb38f996e7ca4c8fc7a84e6dbf7e439fe238a9073","observation_id":"a2c1e83c-5c0f-4d91-887b-3a0aa009567f","resolution":{"observed_at":"2026-05-10T22:15:49.708901Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.04539","last_updated":"2026-05-15T07:15:36Z","snapshot_observed_at":"2026-08-14T16:29:37.712652Z","submitted_at":"2026-04-06T09:03:41Z","title":"FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-19T17:08:31.770889Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.04539"},"observation_digest":"sha256:b481002c04740d9ca82a5a34348842f4dd0588fd184a0f17733651bf60861540","observation_id":"627ae881-2b5f-42d0-9613-b815e006808b","resolution":{"observed_at":"2026-05-19T17:12:41.315328Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.05531","last_updated":"2026-04-07T07:30:21Z","snapshot_observed_at":"2026-08-14T15:43:11.800038Z","submitted_at":"2026-04-07T07:30:21Z","title":"Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T20:04:06.942355Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.05531"},"observation_digest":"sha256:7fc97bc9c0c3dc84b14b96dd48e5bae902da14c928d524e48501fda249493f4a","observation_id":"d1c0e1be-db11-421f-8535-fa344fdcb0b6","resolution":{"observed_at":"2026-05-10T22:15:50.383745Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.13645","last_updated":"2026-04-15T09:14:43Z","snapshot_observed_at":"2026-08-17T03:50:55.250696Z","submitted_at":"2026-04-15T09:14:43Z","title":"A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T12:29:38.306670Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.13645"},"observation_digest":"sha256:99168ccb65777b707c5c7d3d6d70337e5af9eca067ee74be9bb977eee8e2f734","observation_id":"7945ad17-eae3-4831-ba49-324b0c9fb612","resolution":{"observed_at":"2026-05-10T12:30:22.763830Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.24916","last_updated":"2026-05-07T09:03:16Z","snapshot_observed_at":"2026-08-12T17:17:51.797562Z","submitted_at":"2026-04-27T18:51:01Z","title":"asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T02:45:48.819070Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.24916"},"observation_digest":"sha256:2fac126f8b3a2812a63f113e090342eb426edc33cfe50f96d15b899237d4bcdf","observation_id":"0b7b07e6-1325-452d-bb3a-3d873f7f2bcf","resolution":{"observed_at":"2026-05-11T22:26:14.865036Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.24916","last_updated":"2026-05-07T09:03:16Z","snapshot_observed_at":"2026-08-12T17:17:51.797562Z","submitted_at":"2026-04-27T18:51:01Z","title":"asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T03:27:02.669953Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.24916"},"observation_digest":"sha256:4be58f03ac4c72e3b96603b6aef50ad2fbf7af7576e2a18bb98d2dc08e132e96","observation_id":"5f5b781d-d0c8-4574-a622-6feae5ff7257","resolution":{"observed_at":"2026-05-11T22:06:16.506077Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2604.25459","last_updated":"2026-08-04T10:54:55Z","snapshot_observed_at":"2026-08-11T14:02:24.100057Z","submitted_at":"2026-04-28T10:05:39Z","title":"GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-07T16:12:45.211236Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2604.25459"},"observation_digest":"sha256:170d38cf9b532e4906730f204707284d95d12e28424c51959db81f18dab8489a","observation_id":"a1e497f0-6063-4745-85d7-a093f89f590f","resolution":{"observed_at":"2026-05-11T23:51:17.041890Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.19503","last_updated":"2026-05-20T07:37:16Z","snapshot_observed_at":"2026-08-15T10:30:05.399557Z","submitted_at":"2026-05-19T07:54:40Z","title":"ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-20T05:28:50.354662Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.19503"},"observation_digest":"sha256:4b4edceef5b294a43287fd0ed7c46faa89e7b745608e884ef56ea6cb374fa7e4","observation_id":"1c47de61-72df-42d8-b304-69c78636cb4f","resolution":{"observed_at":"2026-05-20T05:33:04.209666Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.19503","last_updated":"2026-05-20T07:37:16Z","snapshot_observed_at":"2026-08-15T10:30:05.399557Z","submitted_at":"2026-05-19T07:54:40Z","title":"ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-21T07:36:12.214949Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.19503"},"observation_digest":"sha256:88cc02e300503432e216d639cb18f7535ff7bb2e15dac9bab69c479b14191201","observation_id":"baaa4e01-c023-44a7-a32e-be4d220f4bc3","resolution":{"observed_at":"2026-05-21T07:39:49.242236Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.24922","last_updated":"2026-05-24T07:57:22Z","snapshot_observed_at":"2026-08-16T04:20:02.143237Z","submitted_at":"2026-05-24T07:57:22Z","title":"MuJoCoUni:Persistent Batched Runtime Primitives for MuJoCo","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T01:13:54.860724Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.24922"},"observation_digest":"sha256:866a9c455120267c8a8ec3122560939df8059c1f84f8c0d50c66ab8e08af07aa","observation_id":"b3607232-2a62-42a6-b2cc-e971d0e1aaf7","resolution":{"observed_at":"2026-07-01T15:45:47.814957Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.24975","last_updated":"2026-05-24T10:04:11Z","snapshot_observed_at":"2026-08-08T13:46:54.260741Z","submitted_at":"2026-05-24T10:04:11Z","title":"Bridging the Gap: Enabling Soft Actor Critic for High Performance Legged Locomotion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-30T00:54:12.099045Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.24975"},"observation_digest":"sha256:69bbfe9685a72df289f93b65de32c0786bb405c9a8dbe07b5d1d2b00c672eae9","observation_id":"eea67457-2825-414d-ae0e-a716ad6e0727","resolution":{"observed_at":"2026-07-01T16:05:49.719887Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.26478","last_updated":"2026-05-26T02:35:08Z","snapshot_observed_at":"2026-08-12T14:23:50.664257Z","submitted_at":"2026-05-26T02:35:08Z","title":"Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T17:34:41.053725Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.26478"},"observation_digest":"sha256:b37490786a0e6364581724e9672e0af19595935a05b3e8dae218484ef8fcf86b","observation_id":"a61681b2-7eac-4a12-8491-f38d87ce9c57","resolution":{"observed_at":"2026-06-29T18:03:48.609847Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.30313","last_updated":"2026-06-02T16:21:55Z","snapshot_observed_at":"2026-07-06T23:39:38.845840Z","submitted_at":"2026-05-28T17:53:50Z","title":"UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T07:09:19.137932Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.30313"},"observation_digest":"sha256:89da5492892c4e97fa214608c7c6ebe54dde5f110dfbab775c74237eed4ba2d6","observation_id":"274fdd30-bc50-4669-be9d-66a72a1599c5","resolution":{"observed_at":"2026-06-29T07:13:16.503863Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2605.31481","last_updated":"2026-05-29T16:07:28Z","snapshot_observed_at":"2026-08-16T15:25:06.355776Z","submitted_at":"2026-05-29T16:07:28Z","title":"Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T22:20:37.637669Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2605.31481"},"observation_digest":"sha256:6a92c41afbe921a9d7c0d8d6689ae5caa1e1fe33c7781d20b43e51466b0ca797","observation_id":"a4a01c17-dfe4-4ad5-a76a-d480c3cba122","resolution":{"observed_at":"2026-06-28T22:22:43.501943Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.02636","last_updated":"2026-06-03T16:09:02Z","snapshot_observed_at":"2026-08-16T23:39:20.965188Z","submitted_at":"2026-05-30T22:17:04Z","title":"Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T18:19:58.499360Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.02636"},"observation_digest":"sha256:0414650533c0ee8067d854eec60710edf482dcda9a17eb2e4eb821dbee7efea8","observation_id":"1c23d7ed-caf9-4752-adf0-0569e307b743","resolution":{"observed_at":"2026-06-28T20:42:38.047426Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.04569","last_updated":"2026-06-03T07:59:40Z","snapshot_observed_at":"2026-08-16T20:17:06.234404Z","submitted_at":"2026-06-03T07:59:40Z","title":"MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T06:20:18.312301Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.04569"},"observation_digest":"sha256:ffcbb4f9ee0aba398039a880bdc976e296427d8edfe996f54921c28d0cfee186","observation_id":"069d9282-7e8c-46a9-adad-cb43d4e823e2","resolution":{"observed_at":"2026-07-02T08:16:47.277639Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.07118","last_updated":"2026-06-08T03:25:26Z","snapshot_observed_at":"2026-08-15T05:11:11.899884Z","submitted_at":"2026-06-05T10:18:24Z","title":"QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T21:55:15.993627Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.07118"},"observation_digest":"sha256:720b446611f1be61f32b8c9b5c45e85ea538abcba418aa555ca99f1be76eadfe","observation_id":"9b571c89-d364-428f-a374-292e2fd19428","resolution":{"observed_at":"2026-07-02T17:37:14.979243Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.10596","last_updated":"2026-06-09T09:01:18Z","snapshot_observed_at":"2026-08-14T22:22:13.741650Z","submitted_at":"2026-06-09T09:01:18Z","title":"Embedding Hybrid Systems into Continuous Latent Vector Fields","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-06-27T14:04:40.078357Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.10596"},"observation_digest":"sha256:f9c658c613e462b87218f34c26b9d1cd1732d39f2aebb0aa3b69713d31df019d","observation_id":"8316a452-9a63-4786-99de-4c2f667cf8b6","resolution":{"observed_at":"2026-07-03T04:17:37.141265Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.11767","last_updated":"2026-06-11T07:02:58Z","snapshot_observed_at":"2026-08-02T07:44:19.588682Z","submitted_at":"2026-06-10T07:46:38Z","title":"Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-27T09:50:02.635507Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.11767"},"observation_digest":"sha256:cd1372fc90ae43faa7e59b0445faabb3a0609ec4b83e47a7d2808c36b1cd3707","observation_id":"c3193287-4333-42ec-a088-9d66bf7d96e0","resolution":{"observed_at":"2026-07-03T10:48:02.597110Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.18594","last_updated":"2026-06-17T01:45:13Z","snapshot_observed_at":"2026-08-17T04:47:48.725576Z","submitted_at":"2026-06-17T01:45:13Z","title":"Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T21:22:53.551550Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.18594"},"observation_digest":"sha256:539ad17637c9eeec13f9f20800ea2adc1acded459bdb8b9749f6b4775fa337be","observation_id":"2dfda7d7-0a30-4c9e-af46-c8d1603b103a","resolution":{"observed_at":"2026-07-04T00:19:12.994967Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.19504","last_updated":"2026-06-17T18:41:52Z","snapshot_observed_at":"2026-08-14T09:08:45.723203Z","submitted_at":"2026-06-17T18:41:52Z","title":"Simulating Robotic Locomotion in Sand: Resistive Force Theory in an Open-Source Physics Engine","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-26T20:45:44.072738Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.19504"},"observation_digest":"sha256:a0f671f5dde2119d7e921dc5c0c102014542032f518b4cb49a23fa06aed9b0bd","observation_id":"f4881233-2679-4885-ad83-f92832e45226","resolution":{"observed_at":"2026-07-04T00:59:21.067835Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.20376","last_updated":"2026-06-19T22:06:44Z","snapshot_observed_at":"2026-08-14T00:39:33.882161Z","submitted_at":"2026-06-18T15:36:13Z","title":"CRAX: Fast Safe Reinforcement Learning Benchmarking","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T18:22:36.891186Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.20376"},"observation_digest":"sha256:900f441c092fedd213b45b86e92546e4a387a778a25d92c0ae2b0276ba19d627","observation_id":"73b9c8a3-f9b9-4561-8e6c-cbb6e6c8f9ec","resolution":{"observed_at":"2026-07-04T03:09:30.117010Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.21086","last_updated":"2026-06-19T04:23:10Z","snapshot_observed_at":"2026-08-03T04:20:46.797599Z","submitted_at":"2026-06-19T04:23:10Z","title":"ReFPO: Reflow Regularization for Flow Matching Policy Gradients","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T14:38:54.754049Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.21086"},"observation_digest":"sha256:a13a8d32e948f3fe6feae0fb9b29311c7a782c757c89dc6f5776ae30dbfe0952","observation_id":"f8da729b-4043-4834-9878-f75dc173af1d","resolution":{"observed_at":"2026-07-04T06:19:37.785912Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.21387","last_updated":"2026-06-19T12:53:37Z","snapshot_observed_at":"2026-08-16T09:01:06.932193Z","submitted_at":"2026-06-19T12:53:37Z","title":"Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T13:57:34.489168Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.21387"},"observation_digest":"sha256:a0371f260872f7daa170362bf22c33b316132f06a0e06760e0f5d24dd831c014","observation_id":"4c37d03e-ff90-4e0a-8964-077c453c63b9","resolution":{"observed_at":"2026-06-26T13:59:30.584615Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.22145","last_updated":"2026-06-20T16:54:36Z","snapshot_observed_at":"2026-08-12T21:27:58.737121Z","submitted_at":"2026-06-20T16:54:36Z","title":"Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T11:39:18.595140Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.22145"},"observation_digest":"sha256:e3b32eeabd1850bac8d7e08498fd22ab0d7db08ef7abe75b10f9d8b5c05098be","observation_id":"040babbf-c22e-432f-98bb-4b4687ef0c68","resolution":{"observed_at":"2026-06-26T11:39:24.547908Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2606.29201","last_updated":"2026-06-28T05:01:27Z","snapshot_observed_at":"2026-08-13T08:39:08.764748Z","submitted_at":"2026-06-28T05:01:27Z","title":"Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T07:49:04.825693Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2606.29201"},"observation_digest":"sha256:4ee2422e78c7eeff8b4f4505f05ef1da2737a6b5e12b30825ed936f3dd1d6276","observation_id":"021a8f26-846d-4e74-b55d-7fcbdfd40bdc","resolution":{"observed_at":"2026-06-30T07:54:22.191604Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2607.00442","last_updated":"2026-07-01T04:57:39Z","snapshot_observed_at":"2026-08-13T07:25:57.864655Z","submitted_at":"2026-07-01T04:57:39Z","title":"Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-02T11:59:04.103002Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2607.00442"},"observation_digest":"sha256:30716d039d446aaeec51a76308f84a6fe23767d1f94992ecfac0b5b324a09242","observation_id":"b38a5207-aeea-4be4-b093-599e7d27c379","resolution":{"observed_at":"2026-07-02T12:06:55.353101Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-07-12T01:30:09.928609Z","title":"Zakka, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03570","last_updated":"2026-07-03T19:19:20Z","snapshot_observed_at":"2026-08-19T08:50:13.188993Z","submitted_at":"2026-07-03T19:19:20Z","title":"Cross-Embodiment Robot Manipulation via a Unified Hand Action Space","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-12T01:30:09.928609Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2607.03570"},"observation_digest":"sha256:15c1cadbb3eca96cfe10f182588298914b2bf261613b78554ded5d3e04840e9e","observation_id":"5e963b18-f5ca-43ae-9527-e34373a971c2","resolution":{"observed_at":"2026-07-12T01:30:09.928609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":"2502.08844","doi":"10.48550/arxiv.2502.08844","metadata_source":"pith","pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mujoco playground","venue":"cs.RO","work_id":"0325a224-ac67-49fb-a451-9fb130f682c9","year":2025},"citing_paper":{"arxiv_id":"2607.06337","last_updated":"2026-07-07T14:34:13Z","snapshot_observed_at":"2026-08-13T18:45:49.462517Z","submitted_at":"2026-07-07T14:34:13Z","title":"OrchardBench: A Physically-Grounded, GPU-Parallel Apple-Orchard Simulation Benchmark for Agricultural Robotics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-08T09:33:49.773169Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2607.06337"},"observation_digest":"sha256:25ee1a3c3026fa15a5cfb8b68a43584e3ccd2b1544a4fca7c732a72d9643fa79","observation_id":"a257f697-0539-4b3c-9940-d9204394965c","resolution":{"observed_at":"2026-07-08T09:34:47.883931Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-01T18:24:35.875407Z","title":"Kahrs and Carmelo Sferrazza and Yuval Tassa and Pieter Abbeel , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.17326","last_updated":"2026-07-19T16:29:13Z","snapshot_observed_at":"2026-08-19T10:39:31.197554Z","submitted_at":"2026-07-19T16:29:13Z","title":"Rethinking the Suitability of Reinforcement Learning Algorithms Under Practical Transfer Constraints","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T18:24:35.875407Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2607.17326"},"observation_digest":"sha256:4fcf1e5baa5dfa451fb4a1162538c7c5763916bdac2b6a7e491cb79dcc603de2","observation_id":"1e155c6f-39bb-42ab-9841-927edd53210b","resolution":{"observed_at":"2026-08-01T18:24:35.875407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-01T07:02:19.931906Z","title":"Zakka, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21588","last_updated":"2026-07-23T17:58:08Z","snapshot_observed_at":"2026-08-13T23:35:04.824871Z","submitted_at":"2026-07-23T17:58:08Z","title":"AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T07:02:19.931906Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2607.21588"},"observation_digest":"sha256:7eac0f768c80b773bbdb50c1afff7a90735b38bfe69474a571040f02c78a4ec5","observation_id":"fd659c5f-e75e-464b-92c9-abe51ca702e2","resolution":{"observed_at":"2026-08-01T07:02:19.931906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-01T00:49:36.679003Z","title":"Zakka, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26055","last_updated":"2026-07-28T17:59:31Z","snapshot_observed_at":"2026-08-18T05:20:26.139820Z","submitted_at":"2026-07-28T17:59:31Z","title":"$\\pi\\mathbf{R}^2$: Reactive Real-time Flow Policies","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T00:49:36.679003Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2607.26055"},"observation_digest":"sha256:0b255cbc0f66fbdcaec9ccab1f8461de453f922a2414aad4b7573a9358f50131","observation_id":"d5868d46-7211-4538-97f8-d5f854ff5616","resolution":{"observed_at":"2026-08-01T00:49:36.679003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-05T00:17:16.320668Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00820","last_updated":"2026-08-01T18:53:02Z","snapshot_observed_at":"2026-08-19T13:28:51.504984Z","submitted_at":"2026-08-01T18:53:02Z","title":"LooperMuscle: Fast and Stable Learning of Humanoid Whole-Body Tracking via Structured Mixture-of-Experts","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T00:17:16.320668Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2608.00820"},"observation_digest":"sha256:5be37b321bd9d2c885e6ff92024b93289aea098d3129825865d8c51e345227c2","observation_id":"6fe240df-fd29-4458-bda9-e87a47ac0078","resolution":{"observed_at":"2026-08-05T00:17:16.320668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-04T07:32:30.427620Z","title":"Zakka, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02433","last_updated":"2026-08-03T16:12:29Z","snapshot_observed_at":"2026-08-14T09:38:19.363180Z","submitted_at":"2026-08-03T16:12:29Z","title":"Foundations of Reinforcement Learning and Control:Connections and New Perspectives","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-04T07:32:30.427620Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2608.02433"},"observation_digest":"sha256:c6854f936df297f48a1491828daaec26356e2b26fa1403ef3abaac222c45f175","observation_id":"496ca157-cab4-4c9c-9a02-a3f9daebf011","resolution":{"observed_at":"2026-08-04T07:32:30.427620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-15T14:42:26.860884Z","title":"Mujoco playground,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05723","last_updated":"2026-08-06T08:06:40Z","snapshot_observed_at":"2026-08-15T14:33:25.092695Z","submitted_at":"2026-08-06T08:06:40Z","title":"ATP: Anatomical Torque with Passivity-based Control Framework for Safe Upper-Limb Exoskeleton Assistance","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T14:42:26.860884Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2608.05723"},"observation_digest":"sha256:d2f0a2acf47241bbe7973518891274db2de55ef7d55b8e1fb39d73862c88a3a0","observation_id":"68d00cd2-65a6-4fa3-afa5-14e64dc04d4f","resolution":{"observed_at":"2026-08-15T14:42:26.860884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08844","snapshot_observed_at":"2026-08-12T00:48:47.076898Z","title":"arXiv preprint arXiv:2502.08844 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.07870","last_updated":"2026-08-08T02:44:43Z","snapshot_observed_at":"2026-08-15T15:49:08.325082Z","submitted_at":"2026-08-08T02:44:43Z","title":"V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control","version":1},"reference_index":284,"source":"arxiv_source","source_observed_at":"2026-08-12T00:48:47.076898Z"},"links":{"cited_paper":"/paper/2502.08844","citing_paper":"/paper/2608.07870"},"observation_digest":"sha256:7ed3cdf8e99778defb40df88acaae691db78e4d297d55d609350be5b5a933f9d","observation_id":"5b8b3ed1-8047-43e6-97c2-5d88df470f2f","resolution":{"observed_at":"2026-08-12T00:48:47.076898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.08844/citation-record","integrity":"/paper/2502.08844/integrity","json":"/paper/2502.08844/citation-record.json","paper":"/paper/2502.08844"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.922149Z","title":"Legged locomotion in challenging ter- rains using egocentric vision","venue":null,"work_id":"bd701195-be8a-4304-8f4e-20eab5271a7f","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.851080Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:0b639581766d5430729447a4219fc9a7e9f8542c47eced6c1b5c8ab5683a88e5","observation_id":"fa9376a8-b7cd-439f-85c6-3d5b511d2d18","resolution":{"observed_at":"2026-08-07T23:34:13.929512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02292","last_updated":"2024-02-07T23:58:10Z","snapshot_observed_at":"2026-08-18T19:25:53.597763Z","submitted_at":"2024-02-07T23:58:10Z","title":"ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.02292","snapshot_observed_at":"2026-08-07T23:34:10.856636Z","title":"Aloha 2: An enhanced low- cost hardware for bimanual teleoperation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.856636Z"},"links":{"cited_paper":"/paper/2405.02292","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:7fdbd328d0ddbfc0d9c1b1f6d167b01b63d884f66693f735517149b7c7fcd419","observation_id":"9ad439b4-ac58-423d-91d5-1bc0d0002a10","resolution":{"observed_at":"2026-08-07T23:34:10.856636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05990","last_updated":"2020-06-10T17:59:03Z","snapshot_observed_at":"2026-08-14T07:14:28.634639Z","submitted_at":"2020-06-10T17:59:03Z","title":"What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05990","snapshot_observed_at":"2026-08-07T23:34:10.862569Z","title":"What matters in on-policy reinforcement learning? a large-scale empirical study","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.862569Z"},"links":{"cited_paper":"/paper/2006.05990","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:442bb95bf63b0aeb61fd64cc53018d541afbe7c3aa9e7dc436a4bc3896d11985","observation_id":"87b35a59-608e-447d-9477-3e97bb1daa7f","resolution":{"observed_at":"2026-08-07T23:34:10.862569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.896099Z","title":"Learning dexterous in-hand manipula- tion","venue":null,"work_id":"a4140e28-ff0b-43a2-8568-6caa2a7e47d0","year":2020},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.867778Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:90389deafb7839998c4e31f166940fc4f8da573444d042a275ffb5c7b22f5f97","observation_id":"ed7408b3-39d1-47b7-b262-95c71e0b1096","resolution":{"observed_at":"2026-08-07T23:34:13.905619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.872779Z","title":"JAX: composable transformations of Python+NumPy programs, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.872779Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:0c5751ce51fc938ff37a7a5e2e22e426e9f22c5771cba60fcf0da6940645768e","observation_id":"8e80af11-6a3d-4509-abea-b9b58a968bff","resolution":{"observed_at":"2026-08-07T23:34:10.872779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14654","last_updated":"2023-05-24T02:49:43Z","snapshot_observed_at":"2026-08-16T15:30:23.955893Z","submitted_at":"2023-05-24T02:49:43Z","title":"Barkour: Benchmarking Animal-level Agility with Quadruped Robots","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14654","snapshot_observed_at":"2026-08-07T23:34:10.878775Z","title":"Bark- our: Benchmarking animal-level agility with quadruped robots","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.878775Z"},"links":{"cited_paper":"/paper/2305.14654","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:a3a532ee721be046d4937cd2a8c0bec9da93f36419bdaa50408b309a75b84aed","observation_id":"f3a609f5-f2c5-49c1-95e3-c4f42d4b6e4d","resolution":{"observed_at":"2026-08-07T23:34:10.878775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.884603Z","title":"Closing the sim-to-real loop: Adapting simulation randomization with real world experience","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.884603Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:3d99e7f0582231fa25efc361251c59c3e8979596982613afb792d81fcb28d203","observation_id":"24bf15bb-17de-44c7-bc89-2c05396331e7","resolution":{"observed_at":"2026-08-07T23:34:10.884603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.889487Z","title":"A system for general in-hand object re-orientation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.889487Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:d4157ecc8ea06142f11fb23cf08dfb0cca04ce5154184a947feb1528580f87e9","observation_id":"17b15cd7-c543-46b4-94f7-d923ed44f1ee","resolution":{"observed_at":"2026-08-07T23:34:10.889487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.747542Z","title":"Extreme parkour with legged robots","venue":null,"work_id":"cdfad834-dada-4902-aea0-ec2daae048ea","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.894221Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:f05e21d9f7dcefc8fcd25dd0c8a957819b9e39a84df817cfabebb46140fd03d3","observation_id":"e533a724-fecc-4274-ac44-ed0cb95a89ff","resolution":{"observed_at":"2026-08-07T23:34:13.759864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10760","last_updated":"2024-03-16T01:47:53Z","snapshot_observed_at":"2026-08-17T03:02:32.504907Z","submitted_at":"2024-03-16T01:47:53Z","title":"CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.10760","snapshot_observed_at":"2026-08-07T23:34:10.899764Z","title":"Corn: Contact-based object represen- tation for nonprehensile manipulation of general unseen objects","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.899764Z"},"links":{"cited_paper":"/paper/2403.10760","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:2be3f48b416fdb34fb2509b9e551aef28a6cc583641a6ad040b5a3a07f7f1d49","observation_id":"dd315f5e-2687-4a76-a2cc-8cefcdfb4714","resolution":{"observed_at":"2026-08-07T23:34:10.899764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.715587Z","title":"Onnx runtime","venue":null,"work_id":"56d3c56a-8c29-477c-b02c-3e3dc9b01acd","year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.905027Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:488ed645a45932601b49a23b64cb345d9b0065939141e6a9b47d528a510acb9a","observation_id":"e2690e60-8b56-4e79-bb01-4e6fb3a9001f","resolution":{"observed_at":"2026-08-07T23:34:13.727976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.910360Z","title":"Flayols, A","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.910360Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:11609b9f25a6321bcecc330d279db4d73d109fc22f9bf3cface60de98732e58a","observation_id":"fae33878-d52d-4882-96ec-920ca0513b62","resolution":{"observed_at":"2026-08-07T23:34:10.910360Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.677635Z","title":"Brax-a differentiable physics engine for large scale rigid body simulation, 2021","venue":null,"work_id":"7d4575c4-1dfe-4add-9da6-a5e445042e94","year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.915072Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:636e7c203f185fbc122c2bb1d78924077a31755f6c74dd189a10afa3e8a7865d","observation_id":"5799c609-edaf-4daf-b68e-38df5f53f7a2","resolution":{"observed_at":"2026-08-07T23:34:13.688721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.646060Z","title":"Genesis: A universal and generative physics engine for robotics and beyond, December 2024","venue":null,"work_id":"d9f54a78-dc02-4bf8-8ddf-bcfb01219f5a","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.919972Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:7f22045e56519946894a995156015ca69fa4633163336815bb91f7b383105adb","observation_id":"d770e87e-6209-4ab7-9464-3816a08ddcdd","resolution":{"observed_at":"2026-08-07T23:34:13.657010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.607490Z","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","venue":null,"work_id":"6b5ae48b-3c2e-4e5f-ae92-d9c8d1300cc6","year":2018},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.925154Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:ab2b832fb76c19f4c81f40cb2059f76b322551962cf6873d7306297e3d9ff8dd","observation_id":"8d763b7c-4a47-48cb-943c-c8f118751cfc","resolution":{"observed_at":"2026-08-07T23:34:13.628768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.929790Z","title":"Learning agile soccer skills for a bipedal robot with deep reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.929790Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:68cdd4ade7c0e8a2acfa04934b4083a657e6331e179dc87dc8457fab0a764256","observation_id":"e3378a3d-945a-4fb8-84fa-d0d77522e814","resolution":{"observed_at":"2026-08-07T23:34:10.929790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04104","last_updated":"2024-04-17T17:41:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-01-10T18:12:16Z","title":"Mastering Diverse Domains through World Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04104","snapshot_observed_at":"2026-08-07T23:34:10.934582Z","title":"Mastering diverse domains through world models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.934582Z"},"links":{"cited_paper":"/paper/2301.04104","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:9db3661ed1bea9e832ae43a95c3409e44db640cf384001bc65b030085e673c1c","observation_id":"691c9860-bb41-4d12-846a-d93bd0d631a3","resolution":{"observed_at":"2026-08-07T23:34:10.934582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.553529Z","title":null,"venue":null,"work_id":"52708960-588d-4552-9a36-7fee979aab82","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.940144Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:33185665d0f26b4b78c0b63a3ee522eb10adfdc8b18ffe31e0ed02c3634b30ec","observation_id":"935dd99f-a9aa-47e8-93b2-5979c989e479","resolution":{"observed_at":"2026-08-07T23:34:13.560878Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.522600Z","title":"Dextreme: Transfer of agile in-hand manipulation from simulation to reality","venue":null,"work_id":"60772216-0f87-4412-a643-0824a98f766a","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.950291Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:3fbc2973e5268ed6144e60a67f2d15604bd9423527df5cabbc9afbc65fab281c","observation_id":"44b7610a-505e-466f-b7f6-4ecff143359b","resolution":{"observed_at":"2026-08-07T23:34:13.530876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.955204Z","title":"Td-mpc2: Scalable, robust world models for continuous control, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.955204Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:40b5b2cbdc8502e546f9ad45770e80c2e88ec72d030bf3b526445a6cda34688e","observation_id":"d10f5a5c-74d9-4f77-a4a2-f69e9a424e1f","resolution":{"observed_at":"2026-08-07T23:34:10.955204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.960123Z","title":"Analytical inverse kinematics for franka emika panda – a geometrical solver for 7- dof manipulators with unconventional design","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.960123Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:3e1d42250eb171ef7e36be4d96a9a5eae829b9e3ebb70021ad8be618b9a8fbc2","observation_id":"bb4627f3-50fb-46a1-a884-8e8230f94f1c","resolution":{"observed_at":"2026-08-07T23:34:10.960123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.465463Z","title":"Evolving control: Evolved high frequency control for continuous control tasks","venue":null,"work_id":"fae0ceb0-a0f5-432e-bb50-cf73b372ba31","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.965210Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:ee6713d60122091d276fa7e08d86a35e9a816f4e97ea19ee4df098ec8eb63ce4","observation_id":"0054a8c2-9ca3-43aa-9d17-62dfd2a3e038","resolution":{"observed_at":"2026-08-07T23:34:13.473859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00935","last_updated":"2020-02-14T06:21:07Z","snapshot_observed_at":"2026-08-10T19:25:02.021953Z","submitted_at":"2019-10-01T05:00:26Z","title":"DiffTaichi: Differentiable Programming for Physical Simulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00935","snapshot_observed_at":"2026-08-07T23:34:10.970819Z","title":"Difftaichi: Differentiable programming for physical sim- ulation","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.970819Z"},"links":{"cited_paper":"/paper/1910.00935","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e39335a1954b364d1d8fdbaefc33896be1f0397279fc75d388662c3735a590cd","observation_id":"f6ec3702-f13b-4b5f-884f-34de4b0b3146","resolution":{"observed_at":"2026-08-07T23:34:10.970819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.433384Z","title":"How to train your robot with deep reinforcement learning: lessons we have learned","venue":null,"work_id":"4ec87f80-637c-4c10-8a60-18b0ad34fe24","year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.976572Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:1967334e165af0b8c88f483e196a3ce0056fbb68da31890fd86d4389aac2a3e3","observation_id":"00660834-3599-431c-8219-4fee5f345484","resolution":{"observed_at":"2026-08-07T23:34:13.440821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.31513","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:11.838686Z","title":"Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion","venue":null,"work_id":"b02b0b51-5097-417c-bb5b-a05bfd13f7ed","year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.981630Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e07453b569f3e350b15152eca22570f3529c584131f29bd433cdb41d8d01bdf7","observation_id":"e5fd55ac-48d6-4f5c-9e10-6e2ab527c8e2","resolution":{"observed_at":"2026-08-07T23:34:11.854545Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.986346Z","title":"Champion-level drone racing using deep rein- forcement learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.986346Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:1857bfa754d08e61d2e142a3b9aa85fbd6044a7afd9955d72c7b3d77ce7d5d4e","observation_id":"14d9023e-45df-48bc-9e6b-17eeaa399a5d","resolution":{"observed_at":"2026-08-07T23:34:10.986346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.373687Z","title":"Reinforce- ment learning in robotics: A survey","venue":null,"work_id":"7f822775-947d-4d69-995f-20338b8de4e7","year":2013},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.993062Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:676dec4f85f4717f9bf2fa2883385b201fe2b456bd0e568078c2e43252dfa914","observation_id":"ceb68d23-e1bf-4d9c-af27-0f8a500cd4d2","resolution":{"observed_at":"2026-08-07T23:34:13.385541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.337583Z","title":"Design and use paradigms for gazebo, an open-source multi-robot sim- ulator","venue":null,"work_id":"e070f421-b575-4ff4-b748-d75e428f6b76","year":2004},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.999033Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:3fe71696200a9d160c6fcdc440746ed02c54bd4d9ec9ae5f7e7145ea36ab43ae","observation_id":"d10424d4-203a-4b74-81e2-8ccf7f0bf569","resolution":{"observed_at":"2026-08-07T23:34:13.345296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14990","last_updated":"2020-11-05T06:04:50Z","snapshot_observed_at":"2026-08-13T23:41:36.426730Z","submitted_at":"2020-04-30T17:35:32Z","title":"Reinforcement Learning with Augmented Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.14990","snapshot_observed_at":"2026-08-07T23:34:11.003868Z","title":"Reinforcement learning with augmented data","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.003868Z"},"links":{"cited_paper":"/paper/2004.14990","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e706ccde93709204fe7f8e1d8306c5a2c90f06c6d11a49c499b5b93e3d406b68","observation_id":"77b33af0-387c-40b6-9719-ece1c4f4f707","resolution":{"observed_at":"2026-08-07T23:34:11.003868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.07517","last_updated":"2019-01-22T18:45:42Z","snapshot_observed_at":"2026-08-14T17:27:29.884529Z","submitted_at":"2019-01-22T18:45:42Z","title":"Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.07517","snapshot_observed_at":"2026-08-07T23:34:11.011005Z","title":"Robust recovery controller for a quadrupedal robot using deep re- inforcement learning","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.011005Z"},"links":{"cited_paper":"/paper/1901.07517","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e57429ff2a7c7464b1a0d57bc312b00ee7b0949ce3119781125dabd0798a6eb8","observation_id":"9570e471-2e99-4075-aa0a-070f3178745d","resolution":{"observed_at":"2026-08-07T23:34:11.011005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.307923Z","title":"rsl rl: Fast and simple implementation of rl algorithms, designed to run fully on gpu","venue":null,"work_id":"63a024fa-200a-4d3a-95ee-2075cca3bd4f","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.016884Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:aa892b488bb411c543e0c14867254fea50ffca1a5e314ea31a9a5ea94c1e51a0","observation_id":"66826b4e-17cc-4718-baca-d9370cc49f35","resolution":{"observed_at":"2026-08-07T23:34:13.316051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14562","last_updated":"2025-03-05T18:55:03Z","snapshot_observed_at":"2026-08-16T13:16:28.143381Z","submitted_at":"2024-09-22T19:00:53Z","title":"DROP: Dexterous Reorientation via Online Planning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14562","snapshot_observed_at":"2026-08-07T23:34:11.021849Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.021849Z"},"links":{"cited_paper":"/paper/2409.14562","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:be85499cbfacb005d33632e80f625cece0a7933324f58f5210755d9b37823927","observation_id":"ab58bfc9-c97b-4375-a64d-44d3782ff0b3","resolution":{"observed_at":"2026-08-07T23:34:11.021849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.275207Z","title":"Rein- forcement learning for versatile, dynamic, and robust bipedal locomotion control","venue":null,"work_id":"1fde7e0e-6b8b-47cf-af5b-9ae50a82d0f4","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.027334Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e2fca2830381944440f2a0b6cf6307b47cde3b8afbf1e1a6b6ccfa6bd7da35e2","observation_id":"e8ceb63d-94d0-4b3b-b425-ab2393557214","resolution":{"observed_at":"2026-08-07T23:34:13.282664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.248029Z","title":"Gpu- accelerated robotic simulation for distributed reinforce- ment learning","venue":null,"work_id":"5e76a5c9-a156-4c9e-aa32-624c4ea734e5","year":2018},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.033633Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e715ecf5c74b9184ecf8c5ddcf24bb85b9734196364c33c4207a1bda06615404","observation_id":"0644c9fd-46ba-454d-a4b8-0025b40929fd","resolution":{"observed_at":"2026-08-07T23:34:13.253941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21781","last_updated":"2024-07-31T17:52:55Z","snapshot_observed_at":"2026-08-17T00:46:45.726434Z","submitted_at":"2024-07-31T17:52:55Z","title":"Berkeley Humanoid: A Research Platform for Learning-based Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21781","snapshot_observed_at":"2026-08-07T23:34:11.038539Z","title":"Berkeley hu- manoid: A research platform for learning-based control","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.038539Z"},"links":{"cited_paper":"/paper/2407.21781","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:f3ef33fa8fe3d738608c76cde94e0d0ea0a185e533ce9bc56a36647b1c1e45a6","observation_id":"f5cc8e5d-fe0a-4a6c-bafa-f7850f672064","resolution":{"observed_at":"2026-08-07T23:34:11.038539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.14386","last_updated":"2024-11-21T18:21:59Z","snapshot_observed_at":"2026-08-12T15:21:03.059579Z","submitted_at":"2024-11-21T18:21:59Z","title":"Learning Humanoid Locomotion with Perceptive Internal Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.14386","snapshot_observed_at":"2026-08-07T23:34:11.043978Z","title":"Learning hu- manoid locomotion with perceptive internal model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.043978Z"},"links":{"cited_paper":"/paper/2411.14386","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:ce0452bc2b9cdd73adb20ce1c7661781ec9a360b2f20c83b60e3e249d99eb730","observation_id":"b655bd5c-76a3-40d5-94ec-4926230a15fb","resolution":{"observed_at":"2026-08-07T23:34:11.043978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12931","last_updated":"2024-04-30T21:35:53Z","snapshot_observed_at":"2026-08-02T10:40:03.816188Z","submitted_at":"2023-10-19T17:31:01Z","title":"Eureka: Human-Level Reward Design via Coding Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.12931","snapshot_observed_at":"2026-08-07T23:34:11.049309Z","title":"Eureka: Human- level reward design via coding large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.049309Z"},"links":{"cited_paper":"/paper/2310.12931","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:9de6efd2b571be680bf513a0cf91d0118884db81b1f10d96a3f1af678af00335","observation_id":"342d6944-cba7-4d91-b2d8-7df0d0d15643","resolution":{"observed_at":"2026-08-07T23:34:11.049309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.205279Z","title":"Warp: A high-performance python frame- work for gpu simulation and graphics","venue":null,"work_id":"f6db319a-2547-41ff-8822-b86bf4bbad14","year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.054659Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:70c7c83b7dae8eba1f876fdf20a1fda244723b07d3d837e8e6356c1429feb064","observation_id":"a4ba11e7-350f-463b-91fa-866a32e4ff96","resolution":{"observed_at":"2026-08-07T23:34:13.218346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.10470","last_updated":"2021-08-25T23:42:59Z","snapshot_observed_at":"2026-07-06T11:40:56.544714Z","submitted_at":"2021-08-24T01:38:11Z","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.10470","snapshot_observed_at":"2026-08-07T23:34:11.060494Z","title":"Isaac gym: High performance gpu-based physics simulation for robot learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.060494Z"},"links":{"cited_paper":"/paper/2108.10470","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:8e79c00eb9a76da903a6060d12c7d19430c7406df483dcdca4a5ca080b94c4e1","observation_id":"8c0cf1ea-4b0e-409e-8339-b5dd660cd4b5","resolution":{"observed_at":"2026-08-07T23:34:11.060494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:11.065799Z","title":"Learning robust perceptive locomotion for quadrupedal robots in the wild","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.065799Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:6060e39c202fd13c00fc94294758d06fc7ec35637976cc8daeadf8d5bd93db1c","observation_id":"5f108b7e-6dcd-4bb8-9d1f-c4314e2c852e","resolution":{"observed_at":"2026-08-07T23:34:11.065799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.144730Z","title":"Orbit: A unified simulation framework for interactive robot learning envi- ronments","venue":null,"work_id":"4d21cf17-46cb-4cfb-82e9-afb3cce97382","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.070919Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:9b787f986c4ec339d5a2e6e4143cb2bbe1f2761b3b93d718c5640650672a3a34","observation_id":"906a61e5-0dc5-4ad2-9e2e-4b71a46d304e","resolution":{"observed_at":"2026-08-07T23:34:13.152950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:11.075894Z","title":"Rusu, Joel Veness, Marc G","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.075894Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:60a451d4d5099c6332d4fe0bc317d85b2995351fdbc239280c1c8f11b5a7b0bb","observation_id":"6573d4d5-0bc9-4911-a552-8f4f3d1d9797","resolution":{"observed_at":"2026-08-07T23:34:11.075894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.119321Z","title":"MuJoCo XLA (MJX)","venue":null,"work_id":"fc116d44-9a10-4e5a-9c6c-90ef1686b511","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.080924Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e53f6401718c76899520a76b70db8dddf609e450344b9be3fc703a579bb63dd1","observation_id":"418792c6-3c60-4e88-b1e2-c50e0fbc594b","resolution":{"observed_at":"2026-08-07T23:34:13.128171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.088245Z","title":null,"venue":null,"work_id":"3007a45e-82d4-4662-b152-eb7d17e91eab","year":2016},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.085861Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:1c4aa33f45d4f9af2b3ab1155a4633d2cd4ef8b77c1e6a30a45f532389586720","observation_id":"1062fdd3-caee-42ab-8ebe-7424b0b1ceaf","resolution":{"observed_at":"2026-08-07T23:34:13.094626Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.048084Z","title":"Dexpbt: Scaling up dexterous manipulation for hand-arm systems with pop- ulation based training","venue":null,"work_id":"f88ef417-97b7-442b-95e1-6ebfd17e47f9","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.091829Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:4273d56e878f1eaf664af136a2e65888e74ed410c8b447df72500de0758aec46","observation_id":"c2137486-9195-4059-b5be-fd090b836d89","resolution":{"observed_at":"2026-08-07T23:34:13.054620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:13.014525Z","title":"Asymmetric actor critic for image-based robot learning","venue":null,"work_id":"ac93f2d1-57ef-44a2-9b3e-0e05dd0cb14c","year":2018},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.096988Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:6d80746f8359c1cacd711333c261b115eb7d7d90f3eeab7e70bd90ba1e17d6d3","observation_id":"2965fe43-a207-4124-84e8-5539bce9b0e8","resolution":{"observed_at":"2026-08-07T23:34:13.024184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03654","last_updated":"2024-10-04T17:57:09Z","snapshot_observed_at":"2026-08-16T13:12:29.520830Z","submitted_at":"2024-10-04T17:57:09Z","title":"Learning Humanoid Locomotion over Challenging Terrain","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03654","snapshot_observed_at":"2026-08-07T23:34:11.104616Z","title":"Learning humanoid locomotion over challenging terrain","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.104616Z"},"links":{"cited_paper":"/paper/2410.03654","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:d4b37912487520f29a5feeab41c7167c871a9f56d88b571e68d70d293041eb3d","observation_id":"62910e05-e945-40be-9c07-91d93d404f63","resolution":{"observed_at":"2026-08-07T23:34:11.104616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05941","last_updated":"2017-10-27T17:45:21Z","snapshot_observed_at":"2026-08-08T18:23:31.977872Z","submitted_at":"2017-10-16T18:05:45Z","title":"Searching for Activation Functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05941","snapshot_observed_at":"2026-08-07T23:34:11.111293Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.111293Z"},"links":{"cited_paper":"/paper/1710.05941","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:dd94d1a6d17f53a5a4c3ed4d41e6bc0e1dfc781f65a7a4108cf97c7fe197bf1e","observation_id":"89b51cdc-e91d-4853-9396-da97800cd9d7","resolution":{"observed_at":"2026-08-07T23:34:11.111293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.986510Z","title":"High-throughput batch rendering for embodied ai","venue":null,"work_id":"d40c1d88-3147-482d-b39f-3a5bb6291961","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.123334Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:62436b6a36e586c4227c7d1c8860fa6e0aaa22e04f1eda9a5a65c8efdb8c3f92","observation_id":"1cc1ce82-6809-4784-a0e3-627ae8ea2de0","resolution":{"observed_at":"2026-08-07T23:34:12.995268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:11.128925Z","title":"Learning to walk in minutes using massively parallel deep reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.128925Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:ce3d62e794c9203dccb6d17a22157b2c15dda9a1e3125c147ca1d21c948fd16f","observation_id":"d967741b-536c-448f-b46a-e25b5a198219","resolution":{"observed_at":"2026-08-07T23:34:11.128925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T23:34:11.134894Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.134894Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:cf325c2d00ce3934685514c06845f6a6c74c3f7c43c580574242515248aac584","observation_id":"d4ce7748-f9da-437f-985d-9a5e305e3d29","resolution":{"observed_at":"2026-08-07T23:34:11.134894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.942653Z","title":"Humanoidbench: Simulated humanoid benchmark for whole-body locomo- tion and manipulation","venue":null,"work_id":"3a4f555c-d6c2-421c-ad87-87b0d6f95fd1","year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.140354Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:28e334c30d3da1ed9cea6c43437184b5a65816c243f991851b72ee0032fe0c1b","observation_id":"68850074-924c-4f0a-9018-c0e978992cdc","resolution":{"observed_at":"2026-08-07T23:34:12.949895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.908529Z","title":"An ex- tensible, data-oriented architecture for high-performance, many-world simulation","venue":null,"work_id":"42e29ef1-1e37-47a0-9dfd-68705dbd60b5","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.145147Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:2cd9b6f4ae5c41c1ea750829178d50d4422e59bacf031da6a8a08fd4b6897054","observation_id":"0c85de97-6627-4729-8e90-c7121be3a591","resolution":{"observed_at":"2026-08-07T23:34:12.920513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.877737Z","title":"An ex- tensible, data-oriented architecture for high-performance, many-world simulation","venue":null,"work_id":"9cdc2716-f195-4b20-af4e-14aad9c3d3a0","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.150542Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:297b1817477b98fbfa98cab3892ed3504d6bc0246318e453687104b85bc7aa59","observation_id":"b5aacce7-50bf-4058-8771-404c27fccc03","resolution":{"observed_at":"2026-08-07T23:34:12.884234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.837044Z","title":"Learning free gait tran- sition for quadruped robots via phase-guided controller","venue":null,"work_id":"148ee6a7-064f-46bd-8bd0-79170664ca60","year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.156529Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:7f3b02fd61ccde29e7ea9def92bce6b10d15a32d17182db68b512fb605eefac8","observation_id":"91b2ba84-6fb0-47be-aa3b-2c15cabe3519","resolution":{"observed_at":"2026-08-07T23:34:12.845238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.806389Z","title":"Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning","venue":null,"work_id":"d1c1c8cd-5b2d-449f-8f98-c3500522ad3d","year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.161472Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:c840ecb89cd790da6ef81a97772c27bc7332f55f67720b60eca00bd86b283e8a","observation_id":"a93b3d01-da96-4cf4-b84d-c063c2b89d7e","resolution":{"observed_at":"2026-08-07T23:34:12.817950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01791","last_updated":"2025-02-01T07:58:23Z","snapshot_observed_at":"2026-08-17T21:54:38.601560Z","submitted_at":"2024-11-27T23:15:06Z","title":"DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01791","snapshot_observed_at":"2026-08-07T23:34:11.166853Z","title":"Dextrah-rgb: Visuomotor policies to grasp anything with dexterous hands","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.166853Z"},"links":{"cited_paper":"/paper/2412.01791","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:cad52501e1ecff7ac72b59ce245440da8011ceb2608eb56a22c63ebc665dc4bd","observation_id":"62ce00af-ffb7-45ac-99e0-340b3e07cad3","resolution":{"observed_at":"2026-08-07T23:34:11.166853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.762405Z","title":"Legged robots that keep on learning: Fine-tuning locomotion policies in the real world","venue":null,"work_id":"424c419b-29c5-4d62-8306-64c7d9f03d6b","year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.172371Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:6090df012be21a22449b082101e8a1f5b75c40edecf9581c43dcc6dfa16899ff","observation_id":"43b14fb3-bd20-4552-b533-ea9c678aeb52","resolution":{"observed_at":"2026-08-07T23:34:12.773652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.10332","last_updated":"2018-05-16T20:35:34Z","snapshot_observed_at":"2026-08-14T19:21:23.179705Z","submitted_at":"2018-04-27T03:42:55Z","title":"Sim-to-Real: Learning Agile Locomotion For Quadruped Robots","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.10332","snapshot_observed_at":"2026-08-07T23:34:11.177602Z","title":"Sim-to-real: Learning agile locomotion for quadruped robots","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.177602Z"},"links":{"cited_paper":"/paper/1804.10332","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:08b0ad2204e4b854428ba3bb72ab6096548307c358da0e4e67311960fed3868f","observation_id":"410bc3d4-9c5d-4ee7-87e6-f1a7aa67bd98","resolution":{"observed_at":"2026-08-07T23:34:11.177602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00425","last_updated":"2025-05-30T05:49:14Z","snapshot_observed_at":"2026-08-16T13:13:51.765058Z","submitted_at":"2024-10-01T06:10:39Z","title":"ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00425","snapshot_observed_at":"2026-08-07T23:34:11.182563Z","title":"Maniskill3: Gpu parallelized robotics simulation and rendering for gener- alizable embodied ai","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.182563Z"},"links":{"cited_paper":"/paper/2410.00425","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:94282ca0b12f674b5c4ef98a3a73cd266f4d87e08f7bf31acd226f8758449666","observation_id":"b2411a8b-6e36-4ac3-ba0a-703c1103808b","resolution":{"observed_at":"2026-08-07T23:34:11.182563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00690","last_updated":"2018-01-02T15:48:14Z","snapshot_observed_at":"2026-08-01T20:24:08.300098Z","submitted_at":"2018-01-02T15:48:14Z","title":"DeepMind Control Suite","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-07T23:34:11.187882Z","title":"Deep- mind control suite","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.187882Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:4d207200a1f5b884cde5037bed557d4843e9d2b5a0fee69bb4b56a6fb0564d30","observation_id":"cf10259e-ff76-491c-bc8b-5636cf06afc0","resolution":{"observed_at":"2026-08-07T23:34:11.187882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.726408Z","title":"Domain ran- domization for transferring deep neural networks from simulation to the real world","venue":null,"work_id":"5268a6d6-0472-42df-b404-611c682757f8","year":2017},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.194861Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:39ba6cb50f2f84347cf543faff74d559ba6a4930fd48545c862af9efc31c91ff","observation_id":"e335c1fd-2f2d-408d-8b1d-807a1bd3436a","resolution":{"observed_at":"2026-08-07T23:34:12.735348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:11.203907Z","title":"Mujoco: A physics engine for model-based control","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.203907Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:5f301273b21986c547b643c701345c06e9de031b6d8bbd3af4c7dac433f216ab","observation_id":"fae85ff3-2bc8-4285-9a0b-80c8f8087eed","resolution":{"observed_at":"2026-08-07T23:34:11.203907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00564","last_updated":"2024-09-12T08:37:27Z","snapshot_observed_at":"2026-08-16T14:13:44.728221Z","submitted_at":"2024-03-01T14:42:25Z","title":"EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.00564","snapshot_observed_at":"2026-08-07T23:34:11.209457Z","title":"Efficientzero v2: Mastering discrete and continuous control with limited data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.209457Z"},"links":{"cited_paper":"/paper/2403.00564","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:1430458e4271183b725d1bfdeb15544d30c91d6ac271161225b02934654f6b42","observation_id":"add7841a-ae70-4bd3-9f32-7480d8d99c7c","resolution":{"observed_at":"2026-08-07T23:34:11.209457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.669950Z","title":"Bench- marking the performance and energy efficiency of ai accelerators for ai training","venue":null,"work_id":"669c1308-237e-49be-b6df-638fcaef2966","year":2020},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.215677Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:2dfa7f0055cfece314f76c5b5414677ab95d98530937c5d46b728abdb1bf0988","observation_id":"29bbe30d-1116-4efb-a6e7-c071fa207b46","resolution":{"observed_at":"2026-08-07T23:34:12.685171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15610","last_updated":"2024-09-23T23:34:37Z","snapshot_observed_at":"2026-08-16T13:16:03.752248Z","submitted_at":"2024-09-23T23:34:37Z","title":"Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15610","snapshot_observed_at":"2026-08-07T23:34:11.222719Z","title":"Full-order sampling-based mpc for torque- level locomotion control via diffusion-style annealing","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.222719Z"},"links":{"cited_paper":"/paper/2409.15610","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:fa943acba41cc4c559f61fc6257e52ecde2fba40c60f003299a17c1c86769fef","observation_id":"5fcdcdeb-4b7c-4e43-acbc-ed90631002d3","resolution":{"observed_at":"2026-08-07T23:34:11.222719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.09645","last_updated":"2021-07-20T17:29:13Z","snapshot_observed_at":"2026-08-16T18:08:43.985218Z","submitted_at":"2021-07-20T17:29:13Z","title":"Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.09645","snapshot_observed_at":"2026-08-07T23:34:11.228893Z","title":"Mastering visual continuous control: Improved data-augmented reinforcement learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.228893Z"},"links":{"cited_paper":"/paper/2107.09645","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:cf381317eac4843edeae18187b7bd55b3446c5e58c4c065cf5e6ccb6c7b61306","observation_id":"716c539f-0f4d-4404-8eb0-acbfdbc9227e","resolution":{"observed_at":"2026-08-07T23:34:11.228893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:11.234964Z","title":"MuJoCo Menagerie: A collection of high- quality simulation models for MuJoCo, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.234964Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:a3557f06562113b7c276a840b9eeb8911053397882c2754aa65f46af6305b249","observation_id":"50f51a09-192b-4464-bc76-ed84f699daa4","resolution":{"observed_at":"2026-08-07T23:34:11.234964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.624874Z","title":"Sim-to-real transfer in deep reinforcement learning for robotics: a survey","venue":null,"work_id":"572eaaf6-75de-4be0-88c2-242402258742","year":2020},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.240472Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:1351b6e3ad2ee005e229b51cce5a63474d703598697d1900bd6ac573b1d045db","observation_id":"97cd2f7b-df40-4362-833c-8e3cce22a425","resolution":{"observed_at":"2026-08-07T23:34:12.633904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05665","last_updated":"2023-09-12T03:01:55Z","snapshot_observed_at":"2026-08-16T15:01:38.241828Z","submitted_at":"2023-09-11T17:59:17Z","title":"Robot Parkour Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05665","snapshot_observed_at":"2026-08-07T23:34:11.246073Z","title":"Robot parkour learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.246073Z"},"links":{"cited_paper":"/paper/2309.05665","citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:3765c070b269e9200df89608469f908014f2505896fa708501b14ecb732cb9c9","observation_id":"85620289-00ba-4918-91ea-30ab4a2728b5","resolution":{"observed_at":"2026-08-07T23:34:11.246073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.589403Z","title":null,"venue":null,"work_id":"e8384eaa-c22b-4bd8-9a87-e25f880664d0","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.252413Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:ad07632d5b0d3fa3a8ad46100c19d50b054ac30d17489a64490bf0d41d65182c","observation_id":"2c91a006-ea14-4b75-94e6-6735b5153476","resolution":{"observed_at":"2026-08-07T23:34:12.601436Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.561642Z","title":null,"venue":null,"work_id":"50999a9b-1e21-4d11-9a7f-9fde1eaab487","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.257818Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:eb63b16baaafab58d1ef270270143248b5f2a0d66919b7d7b9054dbcef88bc8d","observation_id":"74ea8abe-f4e1-4889-a458-205b0991da61","resolution":{"observed_at":"2026-08-07T23:34:12.567978Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.533473Z","title":null,"venue":null,"work_id":"c1bd9066-1ec6-4791-8068-0d24aeccb383","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.264364Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:fcbfd8ab55ac131bb9c78a423345750d8a50090f3690e6915ba3ca4b02ddcdda","observation_id":"a299a52c-802e-4844-bd09-bcc20087fc13","resolution":{"observed_at":"2026-08-07T23:34:12.541681Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.504823Z","title":null,"venue":null,"work_id":"82e03c09-a471-4e60-b5f6-f6e860f8c8c4","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.271284Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:b3434cb9521f834ab153ed1b7622e028d37d7aaa25be1c1052017337aab2dc58","observation_id":"11bcb761-4e63-49f6-8a25-45a9a596f140","resolution":{"observed_at":"2026-08-07T23:34:12.513319Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.476721Z","title":null,"venue":null,"work_id":"2650fc4d-a146-4f4a-a1cb-504447e7da93","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.277260Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:f5f47ac46b0f772a2429e01720895153abed9699534e6e791189f9e702c5908e","observation_id":"c7228853-32c7-40ee-8b33-42669d190ced","resolution":{"observed_at":"2026-08-07T23:34:12.485281Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.441048Z","title":null,"venue":null,"work_id":"b3b3bb01-4164-4ba8-84c4-b8c2e431b7a2","year":null},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.283456Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:e3397e311115dc7bc1c7195d275cc56a0b7cc841bfba5c630168b75fa2f694fe","observation_id":"440a8768-6154-4064-80f7-d4f9fe137af2","resolution":{"observed_at":"2026-08-07T23:34:12.455345Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:12.414029Z","title":"injections","venue":null,"work_id":"f0147b29-29e0-4a8e-921a-b99f32168159","year":2000},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:11.289646Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:a42a358f60224997497caab8f1e272bdb6a24c913d1502b1fd40541b869e2a61","observation_id":"9eece729-f9fe-41d6-a135-d2d0c65524db","resolution":{"observed_at":"2026-08-07T23:34:12.425243Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:34:10.945235Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T23:34:10.945235Z"},"links":{"citing_paper":"/paper/2502.08844"},"observation_digest":"sha256:30c3f90766d5f58518091ea50412a1b033b52d921823b459a139a3254ce54bbf","observation_id":"bda7f170-6c68-4735-9cdd-5a0f194b7468","resolution":{"observed_at":"2026-08-07T23:34:10.945235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.08844","last_updated":"2025-02-12T23:30:01Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-18T10:37:12.291554Z","submitted_at":"2025-02-12T23:30:01Z","title":"MuJoCo Playground"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":45,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":78},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 57 inbound Pith citation observations for arXiv:2502.08844."}