{"as_of":"2026-08-05T04:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fd23f05c26450ed951a0c0ad8214d7eea0e82cf4333074ac4f78d578150283d6","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T10:57:54.273960Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2604.15281/citation-record","integrity":"/paper/2604.15281/integrity","json":"/paper/2604.15281/citation-record.json","paper":"/paper/2604.15281"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.08897","last_updated":"2022-01-12T08:19:29Z","snapshot_observed_at":"2026-07-06T12:09:19.763667Z","submitted_at":"2021-11-17T04:27:01Z","title":"ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data","version":3},"cited_work":{"arxiv_id":"2111.08897","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.08897","snapshot_observed_at":"2026-07-04T06:19:37.435697Z","title":"ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data","venue":"cs.CV","work_id":"0ce910be-ca1c-44c7-b7b1-c5353759d85e","year":2021},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2111.08897","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:c56761e59c14a4232e80a5736420d8beb46207e1d7e2867acc8b94bec08b1682","observation_id":"cdef47f2-0dfd-46fe-a34b-c41f0cd2f789","resolution":{"observed_at":"2026-05-15T10:47:09.105767Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.05800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T21:08:58.820608Z","title":"3d cavla: Leveraging depth and 3d context to generalize vision language action models for unseen tasks","venue":null,"work_id":"10a5d041-6d20-404d-a379-a0782b16f45e","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:158dabf24eee9547f32dc2c764398819b65c200d9cbb44c8e1af849a9ed1bb2f","observation_id":"b36251b5-4153-4c3b-940f-93508df3db71","resolution":{"observed_at":"2026-05-10T11:00:04.174258Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"cited_work":{"arxiv_id":"2410.24164","doi":"10.48550/arxiv.2410.24164","metadata_source":"pith","pith_arxiv_id":"2410.24164","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","venue":"cs.LG","work_id":"f790abdc-a796-482f-a40d-f8ee035ecfc2","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2410.24164","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:971c3bac0a2e605b87c1bc720dcd2a5fcbaffe8bbd70593716e3bcbf7c3a5a8b","observation_id":"16454dfc-ede4-4897-abdf-a79fa81f2f55","resolution":{"observed_at":"2026-05-10T12:38:24.665044Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.06111","last_updated":"2025-11-03T11:52:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-09T15:11:13Z","title":"UniVLA: Learning to Act Anywhere with Task-centric Latent Actions","version":3},"cited_work":{"arxiv_id":"2505.06111","doi":"10.48550/arxiv.2505.06111","metadata_source":"pith","pith_arxiv_id":"2505.06111","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"UniVLA: Learning to Act Anywhere with Task-centric Latent Actions","venue":"cs.RO","work_id":"e05d654d-db73-48f6-9318-381b6798bac9","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2505.06111","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:8c390da30757ee9e4a2a4716b35a9f6ef65137475907b79b6580819cdef7f047","observation_id":"4bce0191-0ecb-4136-8753-78433c8fde5b","resolution":{"observed_at":"2026-05-12T15:28:07.265740Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.01961","last_updated":"2025-07-05T04:00:38Z","snapshot_observed_at":"2026-07-06T21:51:19.351544Z","submitted_at":"2025-07-02T17:59:54Z","title":"AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation","version":3},"cited_work":{"arxiv_id":"2507.01961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.01961","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ac-dit: Adaptive coordination diffusion transformer for mobile manipulation","venue":null,"work_id":"4f066f5a-6596-418d-b003-1e0beceea89d","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2507.01961","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:f6249a83f797deb9df2ad0498e854324b71c0858a12a0501e05ee1dc3b1755b3","observation_id":"f7b915b7-c5ba-4d4c-8f5a-67eac288cece","resolution":{"observed_at":"2026-05-10T11:00:04.145199Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18088","last_updated":"2025-08-27T17:52:42Z","snapshot_observed_at":"2026-08-01T01:17:47.017808Z","submitted_at":"2025-06-22T16:26:53Z","title":"RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation","version":2},"cited_work":{"arxiv_id":"2506.18088","doi":"10.48550/arxiv.2506.18088","metadata_source":"pith","pith_arxiv_id":"2506.18088","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation","venue":"cs.RO","work_id":"9b985126-4a2f-4bdf-b014-2a7524ec634e","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2506.18088","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:357fae02f4f519ffbec8515561cf06e96b8d5a86f082ac9ffa41071c09671175","observation_id":"148ceec3-0164-44af-9210-acc7b562e707","resolution":{"observed_at":"2026-05-11T06:40:27.997500Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"The International Journal of Robotics Research44(10-11), 1684–1704 (2025) 1, 2, 5, 6, 10, 12, 13, 17, 21","venue":null,"work_id":"23da82e6-b262-4a9e-8112-9e00d7ae7c38","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:502881085552a0d080ef8fc20e04a5dd9bb5cc6fcf769fd4b22ce799775dc099","observation_id":"b83820e0-5674-469f-9488-71878be799a1","resolution":{"observed_at":"2026-05-19T17:23:09.480863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Proceedings of the IEEE conference on computer vision and pattern recognition","venue":null,"work_id":"9056e853-1abd-44ee-821b-47260712a303","year":2017},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:b48da7fdf203e5bf73d9d7b8e36725e055e76403e45af68f2c504dc9b9e74965","observation_id":"5542c435-fc5a-405f-8ce6-24ec528ed70c","resolution":{"observed_at":"2026-05-19T17:23:09.446472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"scalable deep reinforcement learning for vision-based robotic manipulation","venue":null,"work_id":"97d0b4ab-7292-4744-8845-453bf3f13e99","year":2018},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:3a9891034cf5ae0706697ecc8952ef4363ada0811e3c568132c2460fd433d5d6","observation_id":"4d07fd35-df70-4e50-be93-c14847b45f98","resolution":{"observed_at":"2026-05-19T17:23:09.463039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:3ad79f5c9e8391c7a11eee56d606a8a196630dec03905082d29cfe66b68d380b","observation_id":"16eeea4a-bd10-432c-b979-d58734bb4cf3","resolution":{"observed_at":"2026-05-10T11:00:04.147681Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.03081","last_updated":"2025-08-25T06:01:46Z","snapshot_observed_at":"2026-07-06T20:46:55.190035Z","submitted_at":"2025-03-05T00:44:12Z","title":"AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons","version":3},"cited_work":{"arxiv_id":"2503.03081","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.03081","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Airexo-2: Scaling up generalizable robotic imitation learning with low-cost exoskeletons","venue":null,"work_id":"04b1978e-d4cf-4fc2-b725-3cae4d4d6821","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2503.03081","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:e580857575f56da0369c7088922ef3b9f633b7f151d547a9053f06a3b0d605b8","observation_id":"5d05b655-7dc6-4f05-bca3-6670e5f162f0","resolution":{"observed_at":"2026-05-10T11:00:04.150182Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.17817","last_updated":"2023-10-19T19:36:31Z","snapshot_observed_at":"2026-07-06T15:48:55.605324Z","submitted_at":"2023-06-30T17:34:06Z","title":"Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation","version":2},"cited_work":{"arxiv_id":"2306.17817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.17817","snapshot_observed_at":"2026-07-04T13:39:51.173105Z","title":"Act3d: Infinite resolution action detection transformer for robotic manipulation","venue":null,"work_id":"b0b4981c-1d77-469e-8362-e084b479955b","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2306.17817","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:d16860bf47bc31c9afeed897b20bb9c3901761236f38929f5aaeef59c630f834","observation_id":"ed69435f-dcf9-48d9-bc85-18bfeee08707","resolution":{"observed_at":"2026-05-10T11:00:04.160181Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.11002","last_updated":"2025-08-20T00:47:05Z","snapshot_observed_at":"2026-07-06T22:13:09.586800Z","submitted_at":"2025-08-14T18:07:40Z","title":"3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation","version":2},"cited_work":{"arxiv_id":"2508.11002","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.11002","snapshot_observed_at":"2026-07-03T12:08:06.338760Z","title":"arXiv preprint arXiv:2508.11002 (2025) 3","venue":null,"work_id":"69f5cd10-b563-4b3e-ad2d-c6bc0f3fec76","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2508.11002","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:c6414dfed0fe347ebe7552e34b1a902fd381bf8e18bee886c59dce1862e696f2","observation_id":"6da73a0d-fb45-4d8a-bd6c-ed2f9e0c4c0e","resolution":{"observed_at":"2026-05-10T11:00:04.152684Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Conference on Robot Learning","venue":null,"work_id":"724d6ac5-5073-40bf-bd36-c7b3ba6a9ac7","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:ab0d576c47f31431bb66de983096093032fc3e3e630f10a05ef9efda1eed91e9","observation_id":"34f9134d-f405-4e38-af38-dabb31df24de","resolution":{"observed_at":"2026-05-19T17:23:09.469883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04659","last_updated":"2023-02-09T14:24:01Z","snapshot_observed_at":"2026-07-06T14:50:03.793337Z","submitted_at":"2023-02-09T14:24:01Z","title":"ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills","version":1},"cited_work":{"arxiv_id":"2302.04659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.04659","snapshot_observed_at":"2026-07-04T13:29:51.807672Z","title":"Maniskill2: A unified benchmark for generalizable manipulation skills","venue":null,"work_id":"515a6a2b-a48e-4c16-af6b-2df6e3c3ea2b","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2302.04659","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:ccceb0b493485bc9c9cefbb468c06bda07a4fe6efe6608c5fb59d5c5b69e8ee4","observation_id":"049ddb34-a1e5-47a0-bef8-0cf2b456f642","resolution":{"observed_at":"2026-05-10T11:00:04.155169Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"International Conference on Intelligent Robots and Systems (IROS) (2024) 18","venue":null,"work_id":"6f2b685d-2bde-413b-bfb1-d8a8a27fa345","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:433048e38a4ae2e425310e7b89557a03755e9617ef358959f9c0ce3e6a9ede7a","observation_id":"20a40b4f-a157-49dc-a957-8f2c4ed9d150","resolution":{"observed_at":"2026-05-19T17:23:09.459898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","venue":null,"work_id":"afc405d9-edc1-4e99-ad0b-755197ec7db2","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:d91d6298146171b82ec18d7731e3085a5e8736826b71eada84e6260b06b3cb65","observation_id":"a2a6c108-34e8-4647-b00d-b3811a158a66","resolution":{"observed_at":"2026-05-19T17:23:09.483093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18623","last_updated":"2024-12-14T18:38:03Z","snapshot_observed_at":"2026-07-06T19:58:09.591787Z","submitted_at":"2024-11-27T18:59:52Z","title":"Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation","version":2},"cited_work":{"arxiv_id":"2411.18623","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.18623","snapshot_observed_at":"2026-07-07T12:33:45.438443Z","title":"Lift3d foundation policy: Lifting 2d large- scale pretrained models for robust 3d robotic manipulation","venue":"cs.CV","work_id":"f99f9fd6-1ee3-411d-9aef-ee7ad585f4ec","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2411.18623","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:2b460a7f801d7a77dc4541272aafe55ddfc61c3ec2512e27d5955431d23e5056","observation_id":"98a2996b-22fe-43da-a87e-9de3aacc7893","resolution":{"observed_at":"2026-05-10T11:00:04.162646Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10885","last_updated":"2024-07-25T14:30:22Z","snapshot_observed_at":"2026-07-06T17:31:17.058043Z","submitted_at":"2024-02-16T18:43:02Z","title":"3D Diffuser Actor: Policy Diffusion with 3D Scene Representations","version":3},"cited_work":{"arxiv_id":"2402.10885","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10885","snapshot_observed_at":"2026-07-10T14:37:15.931790Z","title":"3D Diffuser Actor: Policy Diffusion with 3D Scene Representations","venue":"cs.RO","work_id":"68b58508-d209-4c82-b5da-88ed1178eaaa","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2402.10885","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:cda2901d061087962ccda6fdb4e5b7afbd4bfb13120e08456a6f71ac9f9cf02c","observation_id":"83c0df85-a0ab-4518-94bc-8c53c0540e59","resolution":{"observed_at":"2026-05-17T22:00:05.246520Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19645","last_updated":"2025-04-28T07:49:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-27T00:30:29Z","title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","version":2},"cited_work":{"arxiv_id":"2502.19645","doi":"10.48550/arxiv.2502.19645","metadata_source":"pith","pith_arxiv_id":"2502.19645","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","venue":"cs.RO","work_id":"04f46bb3-4346-47e8-bf09-c75d91f96e87","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2502.19645","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:18398bc045fca4c774b6772e67cb3b89f192d2eee1e954a49872b4639f4155bf","observation_id":"dcd1ebab-129d-48c7-8ae1-f90585adde22","resolution":{"observed_at":"2026-05-11T04:35:33.294794Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09246","last_updated":"2024-09-05T19:46:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-13T15:46:55Z","title":"OpenVLA: An Open-Source Vision-Language-Action Model","version":3},"cited_work":{"arxiv_id":"2406.09246","doi":"10.18653/v1/2022.naacl-main.68","metadata_source":"pith","pith_arxiv_id":"2406.09246","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"OpenVLA: An Open-Source Vision-Language-Action Model","venue":"cs.RO","work_id":"3e7e65c5-5aed-4fe9-8414-2092bcb31cc7","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2406.09246","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:6c1ab5cd77ffb5943a5deaeab35b370497fdf429c43812a22b1b6aad3f75e225","observation_id":"45f9d739-891e-4065-96a1-2b63c71e5264","resolution":{"observed_at":"2026-05-10T14:46:37.168344Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07511","last_updated":"2025-03-10T16:32:41Z","snapshot_observed_at":"2026-07-06T20:50:00.422560Z","submitted_at":"2025-03-10T16:32:41Z","title":"PointVLA: Injecting the 3D World into Vision-Language-Action Models","version":1},"cited_work":{"arxiv_id":"2503.07511","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.07511","snapshot_observed_at":"2026-07-03T15:18:33.003700Z","title":"Pointvla: Injecting the 3d world into vision-language- action models.arXiv preprint arXiv:2503.07511, 2025a","venue":null,"work_id":"9be65aa8-e58c-4756-b5ab-b5e59aac9eae","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2503.07511","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:029b1794f62f95cbef00897d755289ba66d87319e70a286ee7dd67f4aebf8e0e","observation_id":"fc36ecab-8d5d-474f-b84b-f282c404de6f","resolution":{"observed_at":"2026-05-10T11:00:04.190007Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.12276","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T20:16:29.394965Z","title":"Spatial forcing: Implicit spatial representation alignment for vision- language-action model","venue":null,"work_id":"0de0fc11-052f-4fee-af32-850d60b54a52","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:22d3740bf06a17ae592513e88c2d47261694294abe591c0a24b3ebf1295274c9","observation_id":"d2f54ad6-3856-4bef-9829-ee9f91d34b90","resolution":{"observed_at":"2026-05-10T11:00:04.177072Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2506.07961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T17:30:00.489574Z","title":"Bridgevla: Input-output alignment for efficient 3d manipulation learning with vision-language models","venue":null,"work_id":"2cc19a78-3f94-4f0d-a6d5-3a516a9770e2","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:c8c79ea27b9e5c825bd829f5e15af55aa062dbf8299d5367dbe7a3dd906a6224","observation_id":"c45d7175-8301-4ae3-a9cd-4412fb1e5825","resolution":{"observed_at":"2026-05-10T11:00:04.179750Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01378","last_updated":"2024-02-05T03:46:00Z","snapshot_observed_at":"2026-07-31T21:45:51.138808Z","submitted_at":"2023-11-02T16:34:33Z","title":"Vision-Language Foundation Models as Effective Robot Imitators","version":3},"cited_work":{"arxiv_id":"2311.01378","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.01378","snapshot_observed_at":"2026-07-05T11:41:02.629883Z","title":"Vision-Language Foundation Models as Effective Robot Imitators","venue":"cs.RO","work_id":"6eda4437-febe-45ae-889c-8da728146973","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2311.01378","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:90c249c5545eddf3997473cb9d28651084bfcd4dba934ae15e1f8e0f30608d9a","observation_id":"f223ed7b-05f6-4852-b60d-e27e6b6246b4","resolution":{"observed_at":"2026-05-16T21:44:27.700418Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02530","last_updated":"2025-09-02T17:29:38Z","snapshot_observed_at":"2026-07-06T22:22:26.392981Z","submitted_at":"2025-09-02T17:29:38Z","title":"Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots","version":1},"cited_work":{"arxiv_id":"2509.02530","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.02530","snapshot_observed_at":"2026-07-07T22:24:11.123213Z","title":"Manipulation as in simulation: Enabling accurate geometry perception in robots","venue":"cs.RO","work_id":"37b5dc2d-d642-4272-a036-2861dfc3427f","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2509.02530","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:150f8d5e01f5f64290f1f8ef313122d5f241861ac13dbe0a83feae7da2a3b628","observation_id":"3e5a2060-794e-4f64-a91d-6a6d2497a662","resolution":{"observed_at":"2026-05-10T11:00:04.187477Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.07864","last_updated":"2025-03-01T08:57:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-10T12:33:46Z","title":"RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation","version":2},"cited_work":{"arxiv_id":"2410.07864","doi":"10.48550/arxiv.2410.07864","metadata_source":"pith","pith_arxiv_id":"2410.07864","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation","venue":"cs.RO","work_id":"12319725-bc7d-4c32-a229-ad270a7460bc","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2410.07864","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:012c66b8721241bdd96bc89ba97b39279dc6b647a3c4228a2b127c0aeca606dc","observation_id":"84ec40f4-0b56-4eeb-96be-6a1d3ba5f745","resolution":{"observed_at":"2026-05-11T07:46:30.715521Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.15530","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T10:39:45.517996Z","title":"Vo-dp: Semantic-geometric adaptive diffusion policy for vision- only robotic manipulation","venue":null,"work_id":"0adb5dc2-f3eb-42b9-8a1e-7092714a663b","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:730323d361323e3c21acf4c71b16ca607d45ff8d6a49498747c2acc2be45ecbc","observation_id":"6d03199f-1320-477b-97a0-691b8dc8c562","resolution":{"observed_at":"2026-05-10T11:00:04.185050Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: 2024 IEEE International Conference on Robotics and Automation (ICRA)","venue":null,"work_id":"9d8522f8-c43c-49af-b2db-cf2ccef746ae","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:82474dbdf8391810936354a95f28bbce3628cc8f207c9c25f4e0d2d82eb4b8a6","observation_id":"b0ac786e-60b1-4cbb-a268-25bf33462d37","resolution":{"observed_at":"2026-05-19T17:23:09.450662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Proceedings of the IEEE/CVF international conference on computer vision","venue":null,"work_id":"df252fa0-74fc-44f4-8747-54fd4166d5a7","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:2a60108f5bde501d42afa0f20416459fc7fc03714c33d6bf58fef1fa91b82b90","observation_id":"bfe8d01d-15c6-4b32-9342-aaabcec4aa3b","resolution":{"observed_at":"2026-05-19T17:23:09.451017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Proceedings of the IEEE conference on computer vision and pattern recognition","venue":null,"work_id":"b452ef62-8cf8-48c2-8a76-ea35db85f4ee","year":2017},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:12209df12770e6ce1e021fe07dcab24c6da602ce4bdb1238200ce2f39ec2f739","observation_id":"f10e760f-9a07-42ef-a9e1-d71b20196bbb","resolution":{"observed_at":"2026-05-19T17:23:09.472176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Advances in neural information processing systems30(2017) 4","venue":null,"work_id":"8c499b07-68d7-41f4-9102-bed2746c05c8","year":2017},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:9ef53dd0c2b1fa129757133dae44f10583ecf7cf3764d38589ee74dc8dd353b9","observation_id":"cfe46286-d10c-4907-b5a0-b35c303b8e55","resolution":{"observed_at":"2026-05-19T17:23:09.454852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.15733","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T20:16:29.419961Z","title":"Gp3: A 3d geometry-aware policy with multi-view images for robotic manipulation","venue":null,"work_id":"5e43cff1-c150-47e5-b9d0-fc5114749ae3","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:e3bf4d2eed9b508cb90c4635baa4f6d82c758509b902596593447c22df15ab5a","observation_id":"deae8be9-2a7b-4e05-8a6e-dcd045735035","resolution":{"observed_at":"2026-05-10T11:00:04.195087Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18158","last_updated":"2025-03-24T00:39:57Z","snapshot_observed_at":"2026-08-04T01:16:23.160733Z","submitted_at":"2024-06-26T08:17:59Z","title":"3D-MVP: 3D Multiview Pretraining for Robotic Manipulation","version":2},"cited_work":{"arxiv_id":"2406.18158","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.18158","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2406.18158 (2024)","venue":null,"work_id":"5ca980b0-a06a-49ec-b916-508835f9a29d","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2406.18158","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:a2455c46d9739223e75628496e91dbe9c94019c78f51bc8c8fc05970d09119b9","observation_id":"9fec5fc7-e303-487e-8712-84b8de551f9b","resolution":{"observed_at":"2026-05-10T11:00:04.210783Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15830","last_updated":"2025-05-19T02:40:18Z","snapshot_observed_at":"2026-07-06T20:26:31.558337Z","submitted_at":"2025-01-27T07:34:33Z","title":"SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model","version":5},"cited_work":{"arxiv_id":"2501.15830","doi":"10.48550/arxiv.2501.15830","metadata_source":"pith","pith_arxiv_id":"2501.15830","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model","venue":"cs.RO","work_id":"592041b3-3ca2-4836-8dd4-f8095d8a692b","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2501.15830","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:bb319b453798c39d1953604503b29b716bfed5171216cfb266cba201bfbb867b","observation_id":"b45ddd1e-938a-4d3a-b95b-e00d7deca187","resolution":{"observed_at":"2026-05-12T06:12:22.898827Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Conference on Robot Learning","venue":null,"work_id":"d688255f-a128-4c46-8966-1ec169674ab4","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:7db6f6aa80a2ca38986342fb0f08476e85e1191e8ece0c6f605972a463872871","observation_id":"5d8f225e-9e7a-40c5-adfd-b50fb1cc0359","resolution":{"observed_at":"2026-05-19T17:23:09.474165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.09071","last_updated":"2025-08-13T16:47:50Z","snapshot_observed_at":"2026-07-06T22:11:52.522787Z","submitted_at":"2025-08-12T16:46:05Z","title":"GeoVLA: Empowering 3D Representations in Vision-Language-Action Models","version":2},"cited_work":{"arxiv_id":"2508.09071","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.09071","snapshot_observed_at":"2026-07-03T17:38:43.940925Z","title":"Geovla: Empowering 3d representa- tions in vision-language-action models","venue":null,"work_id":"602b66af-32d5-4baa-a248-8f0013efdf1d","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2508.09071","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:1316e1debfd6ca03f3126bb87dce52e0ed18505229dfc140bb73c34a4b6692c8","observation_id":"3c4e72ad-2694-47f3-8c08-22e5abc0a37c","resolution":{"observed_at":"2026-05-10T11:00:04.200200Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12213","last_updated":"2024-05-26T19:55:26Z","snapshot_observed_at":"2026-07-06T18:16:51.116432Z","submitted_at":"2024-05-20T17:57:01Z","title":"Octo: An Open-Source Generalist Robot Policy","version":2},"cited_work":{"arxiv_id":"2405.12213","doi":"10.48550/arxiv.2405.12213","metadata_source":"pith","pith_arxiv_id":"2405.12213","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Octo: An Open-Source Generalist Robot Policy","venue":"cs.RO","work_id":"f9ca0722-8855-48c3-a27a-0eefb7e19253","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2405.12213","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:09366cde73a3827f74792759192ec3a4ab48ec6c086e1e9d107c3530a584f2ec","observation_id":"834f3003-9006-434b-8a91-19e77b444da6","resolution":{"observed_at":"2026-05-11T00:26:16.174119Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.15880","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T16:28:38.355087Z","title":"Improving robotic manipulation with efficient geometry-aware vision encoder","venue":null,"work_id":"27b69496-be05-42f8-877a-d78e93c3a7c3","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:768f84f6f8cc9d883c4b7897ed09e0d772e86cf0401340f88a4bf4b25d3b08bc","observation_id":"40a6a6af-43f8-400e-89ce-e6df241dd0ba","resolution":{"observed_at":"2026-05-10T11:00:04.140225Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.20155","doi":"10.48550/arxiv.2510.20155","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Partnext: A next-generation dataset for fine-grained and hierarchical 3d part understanding","venue":"arXiv (Cornell University)","work_id":"2f3e4995-1fdc-4849-a62b-bf3f0008c6a2","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:a597ec79fc495bf9cb13b18c3d9242f17c00aad2900190a6882792270c72ee76","observation_id":"6e7e1332-520f-4dd8-92b6-99d6f623032a","resolution":{"observed_at":"2026-05-10T11:00:04.124221Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13139","last_updated":"2023-12-21T05:34:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-20T16:00:43Z","title":"Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation","version":2},"cited_work":{"arxiv_id":"2312.13139","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.13139","snapshot_observed_at":"2026-07-04T21:00:09.556391Z","title":"Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation","venue":"cs.RO","work_id":"e92c2c13-4330-45fe-8231-34a6002626bd","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2312.13139","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:023a72c1aeaaec6139acc509bba24192e83aa9aaca1cc553c2c0fd635d874297","observation_id":"d339a71b-c91b-4a66-a577-cb4cb90b0514","resolution":{"observed_at":"2026-05-13T16:32:05.974462Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":"0b583a59-87e4-41c6-97b1-028cc70627da","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:736b375dba0131a24d5ace9cc30a7b73cec03f0c9b5b6871e58fe36c8d7e9dac","observation_id":"c8b356a2-4130-4b3e-9111-6b8c75c88937","resolution":{"observed_at":"2026-05-19T17:23:09.476073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: 2024 IEEE International Conference on Robotics and Automation (ICRA)","venue":null,"work_id":"2dd507c1-dec2-418e-be37-a5a908779fbe","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:03be7f1e5d5d732f5e5389d8c6eb3a20cbfec565ba0823c7fb6aa532d561da87","observation_id":"d35b9d50-961c-4de1-9311-52e0bc26e18a","resolution":{"observed_at":"2026-05-19T17:23:09.467600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.01819","last_updated":"2025-09-01T22:50:55Z","snapshot_observed_at":"2026-08-05T00:40:00.467783Z","submitted_at":"2025-09-01T22:50:55Z","title":"ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training","version":1},"cited_work":{"arxiv_id":"2509.01819","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.01819","snapshot_observed_at":"2026-07-04T09:19:43.498611Z","title":"ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training, September 2025","venue":null,"work_id":"3eabf823-5ce6-4e28-851f-1d05c0dce6ee","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2509.01819","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:ca6b592d5a60338c2a1dcf1cc9c602e7f21e2c54e6c7d9ad1fa8d2d878e93b5b","observation_id":"f5cd087f-5b95-4cfe-83d2-7bdca9e1ff92","resolution":{"observed_at":"2026-05-10T11:00:04.135253Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.08950","last_updated":"2025-03-11T23:01:08Z","snapshot_observed_at":"2026-07-06T20:51:00.484104Z","submitted_at":"2025-03-11T23:01:08Z","title":"FP3: A 3D Foundation Policy for Robotic Manipulation","version":1},"cited_work":{"arxiv_id":"2503.08950","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.08950","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fp3: A 3d foundation policy for robotic manipulation","venue":null,"work_id":"71fe4b22-d036-40e0-aa4d-1b640ab4e6d5","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2503.08950","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:d34d05752e6d5b87a5b208b49131e8acc9793654b6705e39b6ed7f2ddb2bf3b7","observation_id":"8beed491-75ff-4b65-9a28-b7df64563b46","resolution":{"observed_at":"2026-05-10T11:00:04.129404Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Proceedings of the Computer Vision and Pattern Recognition Conference","venue":null,"work_id":"6ce58e68-5a05-4cbc-95f2-aff9fb02b1a6","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:73d5d205139ae35ba95a6d341c6babef90c50b9242b58433c444d3566c83e8c6","observation_id":"4660ae04-8090-4b02-bddf-a64fce59f37b","resolution":{"observed_at":"2026-05-19T17:23:09.465293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.13375","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T00:39:17.350063Z","title":"Depthvla: Enhancing vision-language-action models with depth-aware spatial reasoning","venue":null,"work_id":"16c300ae-4c10-4266-aa06-0541694dec38","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:e0d98d1b17243857892f53f104f3bd49cafdb64333af00389479ae3f02f972bb","observation_id":"2215e6f6-c22d-46ef-9941-328b1dd3e343","resolution":{"observed_at":"2026-05-10T11:00:04.118948Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"arXiv e-prints pp","venue":null,"work_id":"0cd094f8-0bdf-45d1-b158-097a9dba1bff","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:fd3377409efc1635555e8eccc544eace538087a220706b5688478188804e6946","observation_id":"3ddcc228-4145-4ea5-9137-d4e85996bef0","resolution":{"observed_at":"2026-05-19T17:23:09.478534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03954","last_updated":"2024-09-27T02:43:48Z","snapshot_observed_at":"2026-08-01T16:34:39.855742Z","submitted_at":"2024-03-06T18:58:49Z","title":"3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations","version":7},"cited_work":{"arxiv_id":"2403.03954","doi":"10.48550/arxiv.2403.03954","metadata_source":"pith","pith_arxiv_id":"2403.03954","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations","venue":"cs.RO","work_id":"bded01e1-c070-4537-a75a-ace4c75d0c95","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2403.03954","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:ed26fb1a907420dee257aaf44c404405996261f99d5b2af652a325212f613b79","observation_id":"c8f9a52f-a00e-42dc-ae08-f426b8fee9c7","resolution":{"observed_at":"2026-05-15T01:49:37.451338Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13705","last_updated":"2023-04-23T19:10:53Z","snapshot_observed_at":"2026-08-03T01:22:01.078078Z","submitted_at":"2023-04-23T19:10:53Z","title":"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware","version":1},"cited_work":{"arxiv_id":"2304.13705","doi":"10.48550/arxiv.2304.13705","metadata_source":"pith","pith_arxiv_id":"2304.13705","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware","venue":"cs.RO","work_id":"6fe159e0-fa73-481a-88d4-4719c15140be","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2304.13705","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:645dc182529fddf3fbe15fc009b93b46ac17afbe7b8c180ef8197e143f283628","observation_id":"033a75cd-ae37-4b95-bd70-8c54146b4155","resolution":{"observed_at":"2026-05-11T04:15:36.384383Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19269","last_updated":"2025-06-25T05:10:04Z","snapshot_observed_at":"2026-08-04T18:26:05.859380Z","submitted_at":"2025-06-24T03:03:26Z","title":"AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation","version":2},"cited_work":{"arxiv_id":"2506.19269","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.19269","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2506.19269 (2025) 3","venue":null,"work_id":"90efd2e0-2a78-41b0-bfff-d19350102a33","year":2025},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2506.19269","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:979690e00e710bd856863304f6a46f1567bd4c4b90cfcdb71e87aea7a00a3890","observation_id":"59dc6482-eeaf-428e-a841-962d441c369b","resolution":{"observed_at":"2026-05-10T11:00:04.113858Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09631","last_updated":"2024-03-14T17:58:41Z","snapshot_observed_at":"2026-07-06T17:44:45.924645Z","submitted_at":"2024-03-14T17:58:41Z","title":"3D-VLA: A 3D Vision-Language-Action Generative World Model","version":1},"cited_work":{"arxiv_id":"2403.09631","doi":"10.48550/arxiv.2403.09631","metadata_source":"pith","pith_arxiv_id":"2403.09631","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"3D-VLA: A 3D Vision-Language-Action Generative World Model","venue":"cs.CV","work_id":"aebf924c-e761-437e-9cee-f1ccc2e427bd","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2403.09631","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:335c8bcd9c08403e1aa4b123bc892a0d3cdeac5fd9c8934ece9059d3c78b879c","observation_id":"b9ec0899-39dd-410d-a5d3-1e1a2e9d8bf1","resolution":{"observed_at":"2026-05-13T18:18:27.368966Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06773","last_updated":"2023-10-10T16:49:21Z","snapshot_observed_at":"2026-08-04T18:16:57.454117Z","submitted_at":"2023-10-10T16:49:21Z","title":"Uni3D: Exploring Unified 3D Representation at Scale","version":1},"cited_work":{"arxiv_id":"2310.06773","doi":"10.48550/arxiv.2310.06773","metadata_source":"pith","pith_arxiv_id":"2310.06773","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Uni3d: Exploring unified 3d representation at scale","venue":"cs.CV","work_id":"90d21cdb-25b2-450c-8f61-7abddbbcf7e3","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2310.06773","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:e3c2e7f70bb57e5871504031db2dea96ca5c5c71f3a469ccca1041d354ae1838","observation_id":"9cdde1f6-1e40-4314-86ef-236b1d271212","resolution":{"observed_at":"2026-05-10T11:00:04.126873Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17741","last_updated":"2024-12-02T23:28:56Z","snapshot_observed_at":"2026-07-06T18:36:51.761148Z","submitted_at":"2024-06-25T17:28:03Z","title":"Point-SAM: Promptable 3D Segmentation Model for Point Clouds","version":2},"cited_work":{"arxiv_id":"2406.17741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.17741","snapshot_observed_at":"2026-07-08T20:25:37.245999Z","title":"arXiv preprint arXiv:2406.17741 (2024)","venue":"cs.CV","work_id":"23556602-7a7d-483f-8b7f-853eefae7169","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"cited_paper":"/paper/2406.17741","citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:d6f8d7547b13f69a1ebd7176742fece6abe5d195d95eeb2a27984fb9c231f801","observation_id":"5e918120-c927-4bdc-bc5c-261a08872bd7","resolution":{"observed_at":"2026-05-10T11:00:04.142716Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c3e58b9d-f401-4b1c-a870-6820815e2670","year":2024},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:b314e2e2484a3631f579736bbbf017fc280c7680cd45f96a26bdf6597ddf5d46","observation_id":"063c26ab-4754-4fde-b52b-d427ce2d764a","resolution":{"observed_at":"2026-05-19T17:23:09.462871Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"In: Con- ference on Robot Learning","venue":null,"work_id":"f56e1526-6570-439b-8b8a-79a7773f1443","year":2023},"citing_paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-10T10:57:54.273960Z"},"links":{"citing_paper":"/paper/2604.15281"},"observation_digest":"sha256:2f31de4e729e40d4104e8857d3a6872375d9d65c4d074f71b9727108ad68d5b0","observation_id":"cd286903-b954-41e3-b627-fd563f01364c","resolution":{"observed_at":"2026-05-19T17:23:09.453002Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.15281","last_updated":"2026-04-16T17:50:37Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T21:20:28.291912Z","submitted_at":"2026-04-16T17:50:37Z","title":"R3D: Revisiting 3D Policy Learning"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":1,"metadata_mismatch":34,"parse_uncertain":0,"unresolved":1,"verified_exact":5,"verified_fuzzy":15},"total_outbound_references":56},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2604.15281."}