{"as_of":"2026-08-15T22:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6797161deccd3a684c01818a049ee165db318e1345307b3d5bae447c34cbe2c2","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":20,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T16:45:47.228810Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T09:59:44.567814Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2203.12601","last_updated":"2022-11-18T05:57:09Z","snapshot_observed_at":"2026-08-11T08:36:53.636356Z","submitted_at":"2022-03-23T17:55:09Z","title":"R3M: A Universal Visual Representation for Robot Manipulation","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T13:26:53.843613Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2203.12601"},"observation_digest":"sha256:3fa88d540c63d337ae33c90f6875fa200fe1d8ede1212196725e23db28788bc7","observation_id":"0157fca4-8148-4d57-a8a6-6a0d9f09aa33","resolution":{"observed_at":"2026-05-15T13:26:53.937608Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2205.06175","last_updated":"2022-11-11T10:04:29Z","snapshot_observed_at":"2026-08-14T09:46:32.787845Z","submitted_at":"2022-05-12T16:03:26Z","title":"A Generalist Agent","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-13T06:24:49.833638Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2205.06175"},"observation_digest":"sha256:951876ca5aca4157d20fa8c33200b4987f2d2653a88a5b7cbb171389c5241bc2","observation_id":"79b3ba15-3cae-4d0f-9169-be274a19f92c","resolution":{"observed_at":"2026-05-13T06:24:49.987516Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2210.00030","last_updated":"2023-03-07T02:29:59Z","snapshot_observed_at":"2026-08-11T15:42:16.204049Z","submitted_at":"2022-09-30T18:14:07Z","title":"VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T04:42:52.627166Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2210.00030"},"observation_digest":"sha256:729a9e1295e58e8f21feddb7fe9a302822e103fb5b5911a3f1a2c0ef32a35ec4","observation_id":"4679063a-9a08-4a71-8f04-7f14dfc16fc0","resolution":{"observed_at":"2026-05-15T04:42:52.684365Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2310.08864","last_updated":"2025-05-14T15:22:36Z","snapshot_observed_at":"2026-08-13T13:59:48.091257Z","submitted_at":"2023-10-13T05:20:40Z","title":"Open X-Embodiment: Robotic Learning Datasets and RT-X Models","version":9},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-11T17:23:24.255829Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2310.08864"},"observation_digest":"sha256:ccb5bc729edc68cc60fe05270cb7fb3fd021944e9b2c4d86b8b48cc6605e107d","observation_id":"d34ae0ed-cb99-4719-b501-f14ba59a4359","resolution":{"observed_at":"2026-05-11T17:23:24.495456Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2401.02117","last_updated":"2024-01-04T07:55:53Z","snapshot_observed_at":"2026-08-15T12:54:53.410743Z","submitted_at":"2024-01-04T07:55:53Z","title":"Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-14T22:02:55.240949Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2401.02117"},"observation_digest":"sha256:6f278ac1cff86f2a4a9d6fa96c629b4cb192b972d2e3177d189ab957c61cfad5","observation_id":"5988bec5-538e-4261-90a3-12b5f533adfd","resolution":{"observed_at":"2026-05-14T22:02:55.420751Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-12T16:45:47.228810Z","title":"in-the-wild","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.13211","last_updated":"2024-11-21T16:37:32Z","snapshot_observed_at":"2026-08-15T17:03:28.354024Z","submitted_at":"2024-11-20T11:19:22Z","title":"ViSTa Dataset: Do vision-language models understand sequential tasks?","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T16:45:47.228810Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2411.13211"},"observation_digest":"sha256:b0fad6614a9e13d7bbeb2de412e2118187fb4365a655fed721e8db3ada6d670b","observation_id":"a0169577-c9f9-4b4f-aace-dcf08d21b2bf","resolution":{"observed_at":"2026-08-12T16:45:47.228810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-12T11:52:48.296993Z","title":"Learning gener- alizable robotic reward functions from” in-the-wild” human videos","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17820","last_updated":"2025-04-22T01:16:08Z","snapshot_observed_at":"2026-08-12T21:32:50.614159Z","submitted_at":"2024-11-26T19:02:20Z","title":"CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T11:52:48.296993Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2411.17820"},"observation_digest":"sha256:013ae4679a5d652c2a9659ea55f54fb1cbca6fceb5ce7de3e73cdf7bf3b52136","observation_id":"b40ed517-13a4-4be3-9949-97cae728376d","resolution":{"observed_at":"2026-08-12T11:52:48.296993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-11T21:37:57.642287Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04273","last_updated":"2024-12-05T15:55:23Z","snapshot_observed_at":"2026-08-15T09:45:01.086754Z","submitted_at":"2024-12-05T15:55:23Z","title":"Reinforcement Learning from Wild Animal Videos","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T21:37:57.642287Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2412.04273"},"observation_digest":"sha256:f5067546a3470d2abc3aa5cdf87d6c62b33c3daab61d9c025152fd8f8d434248","observation_id":"b6f6f146-ec56-4aca-b6db-7cab7cb66a5a","resolution":{"observed_at":"2026-08-11T21:37:57.642287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-11T17:17:49.492695Z","title":"in-the-wild","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09168","last_updated":"2024-12-12T10:55:57Z","snapshot_observed_at":"2026-08-15T21:45:33.812737Z","submitted_at":"2024-12-12T10:55:57Z","title":"YingSound: Video-Guided Sound Effects Generation with Multi-modal Chain-of-Thought Controls","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-11T17:17:49.492695Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2412.09168"},"observation_digest":"sha256:806ee315878ab43132283a4112f4f6be76ce829d031c8ff532d4c4a4974a3ddd","observation_id":"81cf5fd5-9615-45b6-980a-2e33280f88a0","resolution":{"observed_at":"2026-08-11T17:17:49.492695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-11T11:20:37.537942Z","title":"Learning generalizable robotic reward functions from","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15587","last_updated":"2024-12-20T05:46:29Z","snapshot_observed_at":"2026-08-12T14:28:06.896621Z","submitted_at":"2024-12-20T05:46:29Z","title":"Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T11:20:37.537942Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2412.15587"},"observation_digest":"sha256:d5ddb20f63a87027644d3a06b7d3e747b3542aa3275c0c8648213cf4054aafb0","observation_id":"643f013c-39c2-46a4-a1cd-e881baa18f5c","resolution":{"observed_at":"2026-08-11T11:20:37.537942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-10T19:47:36.407241Z","title":"in-the-wild","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09783","last_updated":"2025-01-16T18:59:51Z","snapshot_observed_at":"2026-08-15T10:36:09.653529Z","submitted_at":"2025-01-16T18:59:51Z","title":"GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T19:47:36.407241Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2501.09783"},"observation_digest":"sha256:f38db8118d5f73e8d47cc4572216e322dd1db3446aa0c67553364b979d654ef3","observation_id":"b78f46e8-539b-49ca-b0d1-c41c2603d924","resolution":{"observed_at":"2026-08-10T19:47:36.407241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-06T23:55:11.576452Z","title":"Learn- ing generalizable robotic reward functions from” in-the- wild” human videos.arXiv preprint arXiv:2103.16817, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.15847","last_updated":"2025-06-18T19:55:10Z","snapshot_observed_at":"2026-08-14T10:31:55.395363Z","submitted_at":"2025-06-18T19:55:10Z","title":"SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:11.576452Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2506.15847"},"observation_digest":"sha256:ab41fa1e1df5edc8a4818cf7152b0d54ab79442f8479682dcede0b1f32d11d5c","observation_id":"5f6196f5-b72e-44af-ac70-123d50d4ef35","resolution":{"observed_at":"2026-08-06T23:55:11.576452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2509.26627","last_updated":"2026-05-20T11:23:00Z","snapshot_observed_at":"2026-08-15T09:06:21.337959Z","submitted_at":"2025-09-30T17:58:20Z","title":"TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-21T21:57:15.285757Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2509.26627"},"observation_digest":"sha256:5aa5d1f21f36ae8db5d3152ff42c0f102be92080f4c152e9ee37ec8dc6527fd6","observation_id":"d59cf4cb-4e8a-4d4c-9d3e-f7661e25c47f","resolution":{"observed_at":"2026-05-21T22:00:41.916824Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2511.04671","last_updated":"2026-04-15T03:42:52Z","snapshot_observed_at":"2026-08-15T18:49:48.326648Z","submitted_at":"2025-11-06T18:56:30Z","title":"X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-18T00:37:12.170711Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2511.04671"},"observation_digest":"sha256:d05504024d18a4cfb7a4b96297369b3c418c6cda91bc91ce9d73b5f2585fd2ab","observation_id":"991e0748-ae0a-4c80-a35e-bc95529f83f8","resolution":{"observed_at":"2026-05-18T00:40:33.725989Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-15T13:07:10.012493Z","title":"Learning generalizable robotic reward functions from","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.07690","last_updated":"2026-06-20T08:02:32Z","snapshot_observed_at":"2026-08-11T13:05:54.065121Z","submitted_at":"2026-03-08T15:46:03Z","title":"FrameVGGT: Coherence-Preserving Memory for Bounded Streaming Geometry","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-15T13:07:10.012493Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2603.07690"},"observation_digest":"sha256:bf93e65919ca0e2f077bcd44e59305b487da2799a2bfb02896641aff20b645ae","observation_id":"60864392-7013-488b-8f4d-f8cf70ae5115","resolution":{"observed_at":"2026-07-15T13:07:10.012493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2606.09777","last_updated":"2026-06-08T17:38:57Z","snapshot_observed_at":"2026-08-11T14:41:55.653504Z","submitted_at":"2026-06-08T17:38:57Z","title":"AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T16:25:09.975308Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2606.09777"},"observation_digest":"sha256:f0f41c292288478dab0465c8094f59300714a9ba7d0476026b9ab7d7a2b7e3ff","observation_id":"14d897ef-48f8-4cfd-8928-43ab2fa79829","resolution":{"observed_at":"2026-07-03T01:37:30.958950Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2606.21406","last_updated":"2026-06-19T13:17:27Z","snapshot_observed_at":"2026-08-03T16:52:45.806990Z","submitted_at":"2026-06-19T13:17:27Z","title":"Robot Self-Improvement via Human-Video Dynamics Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T14:40:13.855741Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2606.21406"},"observation_digest":"sha256:233bdd249f5c935783431f87867630e80a82baa4571001977d84af241d3943df","observation_id":"a2092f97-125a-4b02-8389-1a80d5123506","resolution":{"observed_at":"2026-07-04T06:19:37.510810Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":"2103.16817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-07-04T09:59:44.567814Z","title":"Learning generalizable robotic reward functions from” in-the-wild” human videos.arXiv preprint arXiv:2103.16817","venue":null,"work_id":"01177b56-50d1-4b53-8d24-b3f2b8e7809d","year":2021},"citing_paper":{"arxiv_id":"2606.23640","last_updated":"2026-06-22T17:30:24Z","snapshot_observed_at":"2026-08-02T09:22:48.099895Z","submitted_at":"2026-06-22T17:30:24Z","title":"Learning Process Rewards via Success Visitation Matching for Efficient RL","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T09:20:35.062060Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2606.23640"},"observation_digest":"sha256:11a54f8246e78382770bbf60e8d10aaec5cc0eadb6df05e45347fff1fc24f6cd","observation_id":"6e4e14f5-e887-4a1b-8a87-5c8b562fa0b6","resolution":{"observed_at":"2026-07-04T09:59:44.569657Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-01T17:09:57.739341Z","title":"in-the-wild","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.17760","last_updated":"2026-07-20T09:51:02Z","snapshot_observed_at":"2026-08-15T21:58:18.238033Z","submitted_at":"2026-07-20T09:51:02Z","title":"Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T17:09:57.739341Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2607.17760"},"observation_digest":"sha256:b7af421275939fe7c4cc985a5177511f1ed2208e71743d5709d6f7ad5f35c929","observation_id":"d07a7f70-cb26-415f-9aa5-0c985b13e7f5","resolution":{"observed_at":"2026-08-01T17:09:57.739341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.16817","snapshot_observed_at":"2026-08-04T04:25:52.084807Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.02580","last_updated":"2026-08-03T17:52:26Z","snapshot_observed_at":"2026-08-15T12:33:22.938151Z","submitted_at":"2026-08-03T17:52:26Z","title":"Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T04:25:52.084807Z"},"links":{"cited_paper":"/paper/2103.16817","citing_paper":"/paper/2608.02580"},"observation_digest":"sha256:446a8c19dd942868234ee61e6c0adc09f63328391107aa7deaa4826ea70ea5f1","observation_id":"e689b0c2-5be1-4361-9f74-bf8f0a4ff987","resolution":{"observed_at":"2026-08-04T04:25:52.084807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2103.16817/citation-record","integrity":"/paper/2103.16817/integrity","json":"/paper/2103.16817/citation-record.json","paper":"/paper/2103.16817"},"outbound":[],"paper":{"arxiv_id":"2103.16817","last_updated":"2021-03-31T05:25:05Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-13T19:48:36.525105Z","submitted_at":"2021-03-31T05:25:05Z","title":"Learning Generalizable Robotic Reward Functions from \"In-The-Wild\" Human Videos"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2103.16817."}