{"as_of":"2026-08-08T11:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e5ffe9d0628a45462593b2dbb35fb9705978ad3c4029e139c007b8c17dde63d1","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T13:19:22.518305Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T23:16:04.435902Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.24698","snapshot_observed_at":"2026-07-31T23:16:04.435902Z","title":"Dy- namic policy learning for legged robot with simplified model pretrain- ing and model homotopy transfer,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24036","last_updated":"2026-07-27T06:17:42Z","snapshot_observed_at":"2026-08-07T15:43:50.763464Z","submitted_at":"2026-07-27T06:17:42Z","title":"WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-31T23:16:04.435902Z"},"links":{"cited_paper":"/paper/2512.24698","citing_paper":"/paper/2607.24036"},"observation_digest":"sha256:ac27fcb21dcf44ef16288070c2d5a010038fec423c1764733f1d5f1525a3ca0d","observation_id":"cdabbe07-d89c-4a40-bfce-a705088d2521","resolution":{"observed_at":"2026-07-31T23:16:04.435902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2512.24698/citation-record","integrity":"/paper/2512.24698/integrity","json":"/paper/2512.24698/citation-record.json","paper":"/paper/2512.24698"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:19:16.813811Z","title":"Synthesis and stabilization of complex behaviors through online trajectory optimization,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:16.813811Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:20ad26732579b5f26f4832aed65a65f62dda75d89bc43292b635488dd6d58e3c","observation_id":"a5377c1f-2637-4592-a746-3225b1845c3e","resolution":{"observed_at":"2026-08-03T13:19:16.813811Z","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":"2026-08-03T13:19:16.890302Z","title":"Discovery of complex behaviors through contact-invariant optimization,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:16.890302Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:9915567043fc46e1ed4a2e9d60aa7752bbe3fec5e464b7ec6daf83020177e275","observation_id":"1ec61282-4d4e-4e9a-aed7-8c62d18576fd","resolution":{"observed_at":"2026-08-03T13:19:16.890302Z","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":"2026-08-03T13:19:17.012533Z","title":"Multicontact locomotion of legged robots,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.012533Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:d333e481dede2c0c86d2e592590cbe76adb02c07e65a528168a31b4dc85c9305","observation_id":"2372c488-9506-409c-95a6-1f7ff6b7ce2b","resolution":{"observed_at":"2026-08-03T13:19:17.012533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08961","last_updated":"2024-10-02T08:55:17Z","snapshot_observed_at":"2026-07-06T17:01:44.940193Z","submitted_at":"2023-12-14T14:09:49Z","title":"Contact-Implicit Model Predictive Control: Controlling Diverse Quadruped Motions Without Pre-Planned Contact Modes or Trajectories","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08961","snapshot_observed_at":"2026-08-03T13:19:17.101341Z","title":"Contact-implicit mpc: Controlling diverse quadruped motions without pre-planned contact modes or trajectories,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.101341Z"},"links":{"cited_paper":"/paper/2312.08961","citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:cadf87a0730f13588abccdf0a9fb4c17d7b5b1fd8bdc16b0f28be07981806ab9","observation_id":"f5969b99-7ec2-4d9f-8f96-82ab34b4ba4a","resolution":{"observed_at":"2026-08-03T13:19:17.101341Z","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":"2026-08-03T13:19:17.236644Z","title":"Fast online trajectory optimization for the bipedal robot cassie","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.236644Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:563a0727c9338c5e54e798036e52e95d36b17a0706e7cdd84dd770aa81553d57","observation_id":"013e86ed-04f4-4a2e-95b1-d554e27b7390","resolution":{"observed_at":"2026-08-03T13:19:17.236644Z","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":"2026-08-03T13:19:17.302735Z","title":"Dynamic locomotion in the mit cheetah 3 through convex model-predictive control,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.302735Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:4184042cf98fec64b30c4ae744eef99a3324536ded304d9ae2b8cd8c60947253","observation_id":"1ca59c56-5bca-4ff1-98fc-f855a26c9680","resolution":{"observed_at":"2026-08-03T13:19:17.302735Z","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":"2026-08-03T13:19:17.374713Z","title":"Gait and trajectory optimization for legged systems through phase-based end- effector parameterization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.374713Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:ae5569729062ad9ae46d2bbe6ef9ec67ef8c16d3f02723c7a6c163cc0ec72223","observation_id":"fda51b7a-4b52-4f49-bcfd-a794870e15dd","resolution":{"observed_at":"2026-08-03T13:19:17.374713Z","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":"2026-08-03T13:19:17.451526Z","title":"Real-time constrained nonlinear model predictive control on so (3) for dynamic legged locomotion,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.451526Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:2f021104659f34b8ffb2b26bfc465ae54b67448ecdf442944324a669fe7be490","observation_id":"83b781f3-d6f9-4f91-b1e3-3fe0b723e1e6","resolution":{"observed_at":"2026-08-03T13:19:17.451526Z","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":"2026-08-03T13:19:17.507247Z","title":"Dynamically- consistent trajectory optimization for legged robots via contact point decomposition,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.507247Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:b0c0216c059a838ae56d7e60bb262c7fc68620ba7ca6bcb228518b85ce70a672","observation_id":"24708965-c6ca-48a7-94db-373881a3cc92","resolution":{"observed_at":"2026-08-03T13:19:17.507247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.06586","last_updated":"2019-09-14T12:28:11Z","snapshot_observed_at":"2026-08-04T13:22:09.832632Z","submitted_at":"2019-09-14T12:28:11Z","title":"Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.06586","snapshot_observed_at":"2026-08-03T13:19:17.567978Z","title":"Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.567978Z"},"links":{"cited_paper":"/paper/1909.06586","citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:e3a36a26fe8a5742432e81b889afdb2cf9b43cb8f403bfb2a6a847b6090062c6","observation_id":"1b66a4a4-2483-42e5-a114-0f280aac5d6f","resolution":{"observed_at":"2026-08-03T13:19:17.567978Z","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":"2026-08-03T13:19:17.630935Z","title":"Whole-body motion planning with centroidal dynamics and full kinematics,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.630935Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:3a265bd881887b0dff5d664500c909669cca80cf730348fffb452d4cfe4d2af6","observation_id":"698afcfb-eeb8-4da8-8168-1c9b5c6e9384","resolution":{"observed_at":"2026-08-03T13:19:17.630935Z","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":"2026-08-03T13:19:17.800904Z","title":"Momentum- aware trajectory optimization and control for agile quadrupedal locomo- tion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.800904Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:10e3f2f0062ccc59a7d4d43e836b4feef42d5def607a9b95fb3b4c9f395bffc9","observation_id":"f2208d9e-1fd5-4e04-9fba-298ac219deea","resolution":{"observed_at":"2026-08-03T13:19:17.800904Z","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":"2026-08-03T13:19:17.934986Z","title":"Staged contact optimization: Combining contact-implicit and multi-phase hybrid trajectory optimiza- tion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.934986Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:cd56e7fb9c132d6c3d16ef4d5f00cf1c88d3a74b66a436eaf61bc0679900f6ee","observation_id":"74c22bda-1eea-45ea-a74c-354b101ee92f","resolution":{"observed_at":"2026-08-03T13:19:17.934986Z","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":"2026-08-03T13:19:17.973035Z","title":"Contact-timing and trajectory optimization for 3d jumping on quadruped robots,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:17.973035Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:e035702ce9726cef7ff40d3d979bd8816e71f7fb8e70b8b3e84c93d42a275657","observation_id":"48b5cc81-12f1-4d1a-be4d-e490ccb8df6a","resolution":{"observed_at":"2026-08-03T13:19:17.973035Z","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":"2026-08-03T13:19:18.031738Z","title":"Learning quadrupedal locomotion over challenging terrain,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.031738Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:15a2ba2126853bb0c3d9ec9aca505394b6c1031392ac150f68533c258a17d40c","observation_id":"f6863b29-dd64-4f0c-900a-e6f56f2690ae","resolution":{"observed_at":"2026-08-03T13:19:18.031738Z","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":"2026-08-03T13:19:18.123813Z","title":"Anymal parkour: Learning agile navigation for quadrupedal robots,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.123813Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:5fbc8feb4c9e2aecaf73d5d62afb11cfbabbc7e6090a8cac93c417e10c294d8a","observation_id":"65af9401-cf48-4465-8d95-3fa83bec5e33","resolution":{"observed_at":"2026-08-03T13:19:18.123813Z","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":"2026-08-03T13:19:18.235965Z","title":"High-speed control and navigation for quadrupedal robots on complex and discrete terrain,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.235965Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:d2cd04901ac9128206c79df7b554c625ba0474b4a8de7d14e3dea7a6f2493ee6","observation_id":"85fcd8e8-8b21-4b44-ade2-09c0369af677","resolution":{"observed_at":"2026-08-03T13:19:18.235965Z","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":"2026-08-03T13:19:18.438987Z","title":"Model-free reinforcement learning for robust locomotion using demonstrations from trajectory optimization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.438987Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:f6405e43fb4dfdfd49cebbbd35821d72172d4d104233173f940f40cd43879b7e","observation_id":"f254a53c-39aa-4846-9ee0-7b116fa2b8b6","resolution":{"observed_at":"2026-08-03T13:19:18.438987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.00784","last_updated":"2020-07-21T00:59:24Z","snapshot_observed_at":"2026-07-06T09:09:18.723029Z","submitted_at":"2020-04-02T02:56:16Z","title":"Learning Agile Robotic Locomotion Skills by Imitating Animals","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.00784","snapshot_observed_at":"2026-08-03T13:19:18.581618Z","title":"Learning agile robotic locomotion skills by imitating animals,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.581618Z"},"links":{"cited_paper":"/paper/2004.00784","citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:fce9081ce5d47e18e0db9b54f225039a619736dd8766abf619e89386854cc2f6","observation_id":"227065bb-94ce-4119-9e40-b9863bab1be1","resolution":{"observed_at":"2026-08-03T13:19:18.581618Z","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":"2026-08-03T13:19:18.758270Z","title":"Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.758270Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:dac74f1223fb80c46948d1e7acd7bbcb90087c224680cb1de4aa21ae61b7fdd2","observation_id":"ad7be6fd-902f-4584-b2b5-2eb25f3604bc","resolution":{"observed_at":"2026-08-03T13:19:18.758270Z","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":"2026-08-03T13:19:18.933350Z","title":"Amp: Adversarial motion priors for stylized physics-based character control,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:18.933350Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:60b553d423729e55ace177831897e3014f6214a0c27e013914a75e2a91611eb5","observation_id":"019c440d-5117-4e72-93d3-e39f26f93364","resolution":{"observed_at":"2026-08-03T13:19:18.933350Z","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":"2026-08-03T13:19:19.116239Z","title":"Learning agile skills via adversarial imitation of rough partial demonstrations,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:19.116239Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:e890f684217c11a52a7e6bc543dc6cd7f1f535da2a99175cc39649bf216a34f8","observation_id":"79809dca-05ad-4ad8-b097-ff09227515c0","resolution":{"observed_at":"2026-08-03T13:19:19.116239Z","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":"2026-08-03T13:19:19.278589Z","title":"Learning spring mass locomotion: Guiding policies with a reduced-order model,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:19.278589Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:b770f8ae8635ad41483c779573db53046cf376b3771b19d4f72e221220ca16fe","observation_id":"8425c5de-ea71-4432-b026-17627f6cfccc","resolution":{"observed_at":"2026-08-03T13:19:19.278589Z","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":"2026-08-03T13:19:19.429931Z","title":"Optimizing bipedal maneuvers of single rigid-body models for reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:19.429931Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:fbc3503747e8014807d1d5641052dbe8ab2418b3f12e3aed5663ab438be14acc","observation_id":"4693a991-e31b-46ac-b0c5-2eae36a1e3f2","resolution":{"observed_at":"2026-08-03T13:19:19.429931Z","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":"2026-08-03T13:19:19.606144Z","title":"Opt-mimic: Imitation of optimized trajectories for dynamic quadruped behaviors,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:19.606144Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:4c98717bd6f4e2644962bf179ceec12cc1797251e8c86a6dc94c256e795e7fa9","observation_id":"d19a0fa1-d502-483d-88f2-e3770b6d3e2a","resolution":{"observed_at":"2026-08-03T13:19:19.606144Z","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":"2026-08-03T13:19:19.736103Z","title":"Glide: Generalizable quadrupedal locomotion in diverse environments with a centroidal model,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:19.736103Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:e7b4434327b8d9e47ec375b8d5cfe53463087c46b49d8384356a413e5e41edff","observation_id":"8d3218c9-66f4-4a64-9f9f-6873efb15668","resolution":{"observed_at":"2026-08-03T13:19:19.736103Z","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":"2026-08-03T13:19:19.908439Z","title":"Learning to brachiate via simplified model imitation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:19.908439Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:6322d5bd173b9e550f87ad917a1aee69c130bbca39198aa66817acec3dde5485","observation_id":"f0a8b269-e656-4afc-be19-def95f2aa8f7","resolution":{"observed_at":"2026-08-03T13:19:19.908439Z","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":"2026-08-03T13:19:20.067659Z","title":"Reinforcement learning for reduced- order models of legged robots,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:20.067659Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:15e399884cb030ab49fedcd77088799fed50edc4ee9cb6fde0a0ba0a1d10bcdb","observation_id":"a7395406-6516-42b0-a7dc-fc6dcc94fadd","resolution":{"observed_at":"2026-08-03T13:19:20.067659Z","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":"2026-08-03T13:19:20.222606Z","title":"Adaptive tracking of a single- rigid-body character in various environments,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:20.222606Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:3c6ebe6a8ee93328deae169b63130ae4a22ca2b325165dfbe7d53ea611569080","observation_id":"d6aa134d-7825-4d4a-bf8c-4084584033db","resolution":{"observed_at":"2026-08-03T13:19:20.222606Z","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":"2026-08-03T13:19:20.379954Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:20.379954Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:2bb1259315726b4dc11d85deb76dad825d6ce8af42f3068abdbbfaec7652095b","observation_id":"30c7de5a-1450-4775-a0c9-38ec392765d5","resolution":{"observed_at":"2026-08-03T13:19:20.379954Z","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":"2026-08-03T13:19:20.530912Z","title":"Learning symmetric and low-energy locomotion,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:20.530912Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:c9c3e373918ea9cac6597f445ad020a94adf5c62874752e03fcedba0d49d7dc1","observation_id":"06a676ef-5ef2-4d22-be8f-5344be6f64c8","resolution":{"observed_at":"2026-08-03T13:19:20.530912Z","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":"2026-08-03T13:19:20.678702Z","title":"Robot parkour learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:20.678702Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:a13955f5ceacb9c93b399076352ee52c46d0adc0c172f40590efa544f325d9fa","observation_id":"1de2b076-ad45-492a-accd-34a518b7669b","resolution":{"observed_at":"2026-08-03T13:19:20.678702Z","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":"2026-08-03T13:19:20.825522Z","title":"Not only rewards but also constraints: Applications on legged robot locomotion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:20.825522Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:98456f46438e3137b630249c0c6099280b284042df21bd51f32844486e43e7c2","observation_id":"58986956-dfdd-4c81-86f8-1bad81857949","resolution":{"observed_at":"2026-08-03T13:19:20.825522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15780","last_updated":"2025-05-09T07:26:51Z","snapshot_observed_at":"2026-08-05T13:32:57.356034Z","submitted_at":"2024-09-24T06:22:28Z","title":"A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15780","snapshot_observed_at":"2026-08-03T13:19:21.002320Z","title":"A learning framework for diverse legged robot locomotion using barrier-based style rewards,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.002320Z"},"links":{"cited_paper":"/paper/2409.15780","citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:6b070686443c41cd529c6b692c08283d52b7d52ffce38cc4418740749b9bfc52","observation_id":"5d359473-57c6-4a58-bd81-bfb058156ce1","resolution":{"observed_at":"2026-08-03T13:19:21.002320Z","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":"2026-08-03T13:19:21.144810Z","title":"Imitating and finetuning model predictive control for robust and symmetric quadrupedal locomotion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.144810Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:e4f73b312ee4a3622146152588f3ad7205923c8498b0fca548da09df3bf19b73","observation_id":"1f86191b-d0c7-4812-a7cb-bb42105f1923","resolution":{"observed_at":"2026-08-03T13:19:21.144810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12490","last_updated":"2024-08-22T15:29:12Z","snapshot_observed_at":"2026-08-05T12:37:14.820795Z","submitted_at":"2024-08-22T15:29:12Z","title":"Probabilistic Homotopy Optimization for Dynamic Motion Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12490","snapshot_observed_at":"2026-08-03T13:19:21.281276Z","title":"Probabilistic homotopy optimization for dynamic motion planning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.281276Z"},"links":{"cited_paper":"/paper/2408.12490","citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:c67f0f02f0f379c39b7ab2b305ff6c9a9f76aea8d5d53fc9a6b976ba97423d4a","observation_id":"932249d2-bdae-496e-95da-d3c1580dfc34","resolution":{"observed_at":"2026-08-03T13:19:21.281276Z","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":"2026-08-03T13:19:21.454980Z","title":"Generating families of optimally actuated gaits from a legged system’s energetically conservative dynam- ics,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.454980Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:059468e6d5a0441b42bebdaf9ed518039e35268c81312b6e6cdbc6fda8a780af","observation_id":"d1ca1883-0351-430a-a453-4142ddd5ff25","resolution":{"observed_at":"2026-08-03T13:19:21.454980Z","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":"2026-08-03T13:19:21.589312Z","title":"A survey on curriculum learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.589312Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:4de231e79eada6887bb121f8722070db7efe0723e98f5257c1fdc0a5b126746e","observation_id":"e4cd11cf-5968-42b2-885a-ac4fe55c2aa8","resolution":{"observed_at":"2026-08-03T13:19:21.589312Z","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":"2026-08-03T13:19:21.746469Z","title":"Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.746469Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:fca8a83cf22ba2b799798fb92d60e0a90abb50a617be16671a96f4c8cbfc8a88","observation_id":"bcc36362-bd7b-4b84-80b4-23927c7a66b9","resolution":{"observed_at":"2026-08-03T13:19:21.746469Z","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":"2026-08-03T13:19:21.963944Z","title":"Shin, T.-G","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:21.963944Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:76ab4902d1c54b705a194551cf71ede97e74b848eb0147d60228fe7bea0d7a52","observation_id":"dc0b43d4-dea3-4b16-9acd-9cc82da603b1","resolution":{"observed_at":"2026-08-03T13:19:21.963944Z","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":"2026-08-03T13:19:22.130454Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:22.130454Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:92d6cdae8fb1672ea09f02e11d75ca870012fec8356697e03467447b76ff1426","observation_id":"d92c6061-90b0-432c-bc67-0204616f10c5","resolution":{"observed_at":"2026-08-03T13:19:22.130454Z","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":"2026-08-03T13:19:22.299947Z","title":"Key air phase timings are summarized in Table II","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:22.299947Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:0fb1ece17c80e278b93ca4ed428920df4652017920714c580df5885ac2484c72","observation_id":"d417e766-1ba5-4c36-a2a3-882c72f14d9e","resolution":{"observed_at":"2026-08-03T13:19:22.299947Z","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":"2026-08-03T13:19:22.518305Z","title":"The contact plan is illustrated in Fig","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T13:19:22.518305Z"},"links":{"citing_paper":"/paper/2512.24698"},"observation_digest":"sha256:e5efa989a4def82f23b822e0f17d068e3b4eba7f635ea5456b91fee38cbc6f97","observation_id":"f9843e75-42cb-449f-9af5-6fd60fd23aa1","resolution":{"observed_at":"2026-08-03T13:19:22.518305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.24698","last_updated":"2026-06-03T01:24:56Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T07:21:12.919178Z","submitted_at":"2025-12-31T08:04:22Z","title":"Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":43,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":43},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2512.24698."}