{"as_of":"2026-08-08T01:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dc81054f8cb6dceb5c748c8f5d7a29f26bfc07b348088fc63194c6efdc2feb19","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:32:18.948562Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2505.14819/citation-record","integrity":"/paper/2505.14819/integrity","json":"/paper/2505.14819/citation-record.json","paper":"/paper/2505.14819"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:23.276849Z","title":"Learning dexterous in-hand manipulation,","venue":null,"work_id":"d0f596c1-69a7-42dd-8573-7ae4b022d832","year":2020},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.273016Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:559b9c7bfbf422b9248ff268a63622ecaf9d421e9a4dcb2eac8772e4996d4706","observation_id":"e2f2b105-a202-4767-9a4e-3abd7cb440a1","resolution":{"observed_at":"2026-08-07T15:32:23.427997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:23.080208Z","title":"Learning dexterous grasping with object-centric visual affordances,","venue":null,"work_id":"77825e53-661b-4a48-873f-d2b30f6fe009","year":2021},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.332872Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:e11ffa75e3d970a2d39f3f98c20e1cd697fcb49cf71b81e4e5761f71520dc943","observation_id":"5b22b161-f51a-4641-96e2-ccb13d30cf51","resolution":{"observed_at":"2026-08-07T15:32:23.140796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08844","last_updated":"2025-06-24T09:49:27Z","snapshot_observed_at":"2026-07-06T17:59:38.202928Z","submitted_at":"2024-04-12T23:11:36Z","title":"ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08844","snapshot_observed_at":"2026-08-07T15:32:16.430473Z","title":"Multi-fingered robotic hand grasping in cluttered environ- ments through hand-object contact semantic mapping,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.430473Z"},"links":{"cited_paper":"/paper/2404.08844","citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:29944bfd188ce339e951297078e07e0dd6616b78c3c982932266e3217a5877e4","observation_id":"c5b64890-bc2c-4a1e-af7b-998fb3aa37ab","resolution":{"observed_at":"2026-08-07T15:32:16.430473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:22.800979Z","title":"Learning generalizable dexterous manipulation from human grasp affordance,","venue":null,"work_id":"dba1dd06-7cba-4305-a889-cc7d57b4e5ae","year":2023},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.528582Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:db43f0a5806ca2e8147472eabb72b9dde5f99898fa4c2be6ed5e1273ada8c181","observation_id":"98a86b52-3535-4f5a-b556-415476d4fb15","resolution":{"observed_at":"2026-08-07T15:32:22.997442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:22.386429Z","title":"The ecological approach to visual perception,","venue":null,"work_id":"c9a52751-01d7-482b-a674-3897285f226f","year":1979},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.665284Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:01c1cea5ad51245bc5676fc1e0294e0f44648a33f9257735793b361fba1aa115","observation_id":"e70fee54-dfd3-4541-8e47-c718db0f435e","resolution":{"observed_at":"2026-08-07T15:32:22.627181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:22.050708Z","title":"Affordancenet: An end-to-end deep learning approach for object affordance detection,","venue":null,"work_id":"c4d6ba01-b040-471d-ba8b-568d6ebca892","year":2018},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.781375Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:c9e9cf4f4bb6efec7b5cd11d9268c3d3bb6f3b3a9cc442aedd6059772f2f3735","observation_id":"a8b3d99d-a6eb-406a-b4e1-34da83fc97c3","resolution":{"observed_at":"2026-08-07T15:32:22.216772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.08686","last_updated":"2022-10-11T09:08:26Z","snapshot_observed_at":"2026-07-06T13:21:57.238438Z","submitted_at":"2022-06-17T11:09:06Z","title":"Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.08686","snapshot_observed_at":"2026-08-07T15:32:16.822180Z","title":"Towards human-level bimanual dexterous manipulation with reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.822180Z"},"links":{"cited_paper":"/paper/2206.08686","citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:0d52780cf9943c2b2c4b130d54c0c81590c65e998459330d9a9d2b9aa5aac75c","observation_id":"f6f32512-bfbd-445b-a6b9-48e064543c63","resolution":{"observed_at":"2026-08-07T15:32:16.822180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.00987","last_updated":"2023-10-16T05:05:56Z","snapshot_observed_at":"2026-07-06T16:13:38.508734Z","submitted_at":"2023-09-02T16:55:48Z","title":"Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.00987","snapshot_observed_at":"2026-08-07T15:32:16.931480Z","title":"Sequential dexterity: Chaining dexterous policies for long-horizon manipulation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:16.931480Z"},"links":{"cited_paper":"/paper/2309.00987","citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:f883aa27d6467088ce2c1c4aa9b64ba918717c998d031a24842a2f8f5689d49b","observation_id":"2faf1345-b104-494d-924e-e00bcaa8f954","resolution":{"observed_at":"2026-08-07T15:32:16.931480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:21.758336Z","title":"Bi-dexhands: Towards human-level bimanual dexterous manipulation,","venue":null,"work_id":"fd6c0f99-b43b-47a8-b1c5-e4ae30109141","year":2023},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.054300Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:8f22316e0dbf402cb7d216fcff990647858d519afbad1adb95e51e8b2b9fafa3","observation_id":"9eab0f50-e3dc-4954-8d59-b57d61080cab","resolution":{"observed_at":"2026-08-07T15:32:21.876718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:21.485179Z","title":"Object-based affordances detection with convolutional neural net- works and dense conditional random fields,","venue":null,"work_id":"b461132c-7e1c-4866-b2b9-ddd18c016bd3","year":2017},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.172839Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:fd81f05d744c021497aa99a204b5b52aea36463b69070b3aad2009a0bfaf86b3","observation_id":"06ad9d91-944c-4840-9435-5f0b888b250a","resolution":{"observed_at":"2026-08-07T15:32:21.607804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:21.312588Z","title":"Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching,","venue":null,"work_id":"62631815-ce40-45b0-8be6-bf64f53985d4","year":2022},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.272509Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:86036335e41156d75a7783dec240faa1c3b972d09cbf28a4cc1ca264f9d7e2f7","observation_id":"20ab7341-d5e1-4b47-8ed1-50f8f4d0fab3","resolution":{"observed_at":"2026-08-07T15:32:21.402924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:21.103646Z","title":"Tooleenet: Tool affordance 6d pose estimation,","venue":null,"work_id":"266a36a6-f208-47b9-a2b8-ebeee4da28f3","year":2024},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.402323Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:62bff73dcdafaacd9e7f4c3e5404ddca6e12f33469f3e73e42d31c481016ffb4","observation_id":"6d0c37a5-9949-4303-97b1-982b77eb789b","resolution":{"observed_at":"2026-08-07T15:32:21.197573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10341","last_updated":"2025-07-25T18:09:11Z","snapshot_observed_at":"2026-07-06T18:46:09.997234Z","submitted_at":"2024-07-14T21:41:29Z","title":"Affordance-Guided Reinforcement Learning via Visual Prompting","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10341","snapshot_observed_at":"2026-08-07T15:32:17.503399Z","title":"Affordance- guided reinforcement learning via visual prompting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.503399Z"},"links":{"cited_paper":"/paper/2407.10341","citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:56b907b23811cdfdef52428b46a41fdaf682f9c5b351f390cfd0c4d1326591d2","observation_id":"a3d5add6-e938-43b4-a642-89cf79e4dadb","resolution":{"observed_at":"2026-08-07T15:32:17.503399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:20.875057Z","title":"Affordance learning from play for sample-efficient policy learning,","venue":null,"work_id":"1c99a79a-f2a2-42f4-adee-ac51c8bbeeb0","year":2022},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.632150Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:440ffdc6df9b66394e1c3ac5d895cb07024c8baf827489126a0cfd2e92dd1578","observation_id":"20e6844a-cf87-4448-a3e3-12081224b541","resolution":{"observed_at":"2026-08-07T15:32:20.979850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:20.621978Z","title":"Learn- ing object affordances: from sensory–motor coordination to imitation,","venue":null,"work_id":"7dc38178-457f-4faf-8722-e26db9c6ec2f","year":2008},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.800487Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:f8972da0c06c1ea1172f1261acc552c80336d63bdb2f25a27089bf765e06438c","observation_id":"68b5e541-c9ef-4450-86f7-11da1f9b488d","resolution":{"observed_at":"2026-08-07T15:32:20.762152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:20.398069Z","title":"Grasping affor- dances with the other’s hand: a tms study,","venue":null,"work_id":"74d66de7-5ecc-4c91-9062-8a0106685415","year":2013},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.882550Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:dfcdd42517f610f4990f7079d7fe3dcd9921abe7b25e84ad37bd3c698401845f","observation_id":"5039da48-d42f-45aa-90d3-b5a20ba873cd","resolution":{"observed_at":"2026-08-07T15:32:20.494035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:20.204868Z","title":"Cross-category functional grasp transfer,","venue":null,"work_id":"bdcbc780-83a6-44bb-a4dd-51df4e9a6e8a","year":2024},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:17.998247Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:90a71cefc149b467d7bf8d4a6bf4451e96439541e375167002f9099591947827","observation_id":"12beeffb-5314-4084-916d-301d8ded8445","resolution":{"observed_at":"2026-08-07T15:32:20.282153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:20.052089Z","title":"Task-oriented grasping with point cloud representation of objects,","venue":null,"work_id":"5980920c-9598-4041-8b72-ecf82b7c9f27","year":2023},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.133469Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:1d26dacaaaccad74cd3974dc484210071534631a8e97f4486ca9f8445ba3cd74","observation_id":"a0588b9f-1a9b-4702-96ca-ceb9ffac759b","resolution":{"observed_at":"2026-08-07T15:32:20.120853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:19.879450Z","title":"Constrained policy op- timization,","venue":null,"work_id":"187f8605-5e54-48eb-b8ed-e2634b7e6a45","year":2017},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.211704Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:1f6fad265462beeb623f2106e9d0011b519eaca897292cb21cdfba0e0984cc69","observation_id":"0e667c82-caf6-4934-b75e-43450df5ae52","resolution":{"observed_at":"2026-08-07T15:32:19.954582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:19.711580Z","title":"Safe exploration in finite markov decision processes with gaussian processes,","venue":null,"work_id":"d064ed0d-d9f8-4bc9-be8b-f07abb1e60fa","year":2016},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.293339Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:c1f82600b1a2a49088111da71d3214ee8d43586fe2ed5e657d6d6a76bf274fb7","observation_id":"5b8cc082-ab00-4c30-b35a-0994eb559a96","resolution":{"observed_at":"2026-08-07T15:32:19.809309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:18.415882Z","title":"Safe model-based reinforcement learning with stability guarantees,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.415882Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:e02355736fb8a81f27d4dfde103f317bba4a9010211edf9c477c91e0b46fdfb9","observation_id":"ad399503-7be8-430d-874b-6eb74303644d","resolution":{"observed_at":"2026-08-07T15:32:18.415882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:18.497213Z","title":"Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.497213Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:51ee71de518d23cd85ece34b1a42fad75c389d54196f61e3178b019101cccfe8","observation_id":"4c786bf9-090e-4d3d-a535-4bf11d32f0e1","resolution":{"observed_at":"2026-08-07T15:32:18.497213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:19.417463Z","title":"Multisensory five-finger dexterous hand: The dlr/hit hand ii,","venue":null,"work_id":"9ffefa7f-82fb-460a-bbf4-1763849a3986","year":2008},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.620496Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:d8614f9ac07e79d267ad350842cd3d96f6f9ce95a04bf9b9684c7890232ff1e7","observation_id":"e87b7eb5-b3f9-4ce8-87a2-0dbfb472f136","resolution":{"observed_at":"2026-08-07T15:32:19.533356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.10470","last_updated":"2021-08-25T23:42:59Z","snapshot_observed_at":"2026-07-06T11:40:56.544714Z","submitted_at":"2021-08-24T01:38:11Z","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.10470","snapshot_observed_at":"2026-08-07T15:32:18.757382Z","title":"Isaac gym: High performance gpu-based physics simulation for robot learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.757382Z"},"links":{"cited_paper":"/paper/2108.10470","citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:635b8134e7ba22d6d6970fbd688650bb6a04f771f0cf26e991735ed8c89bbaa3","observation_id":"b1ba8528-260b-4296-8406-9d7ec292700b","resolution":{"observed_at":"2026-08-07T15:32:18.757382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T15:32:18.867108Z","title":"Proximal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.867108Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:9d7260737e2bed2a412e31fb700ad0f4249f00f60d34c0930328d6e04ed27e7e","observation_id":"10594ec8-23cd-4c21-a64a-0e81aaf7027f","resolution":{"observed_at":"2026-08-07T15:32:18.867108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:32:19.234235Z","title":"Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,","venue":null,"work_id":"4bb0736a-1030-491a-9f55-c2153766329c","year":2018},"citing_paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:18.948562Z"},"links":{"citing_paper":"/paper/2505.14819"},"observation_digest":"sha256:188e5139357e206196b687fb4c3484fdfc5231989c8ed8803d748aa1077115f7","observation_id":"1e194da6-b2ec-4aae-9368-fe8e69250f25","resolution":{"observed_at":"2026-08-07T15:32:19.283989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.14819","last_updated":"2025-05-20T18:36:01Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T15:26:51.676783Z","submitted_at":"2025-05-20T18:36:01Z","title":"DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":26},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2505.14819."}