{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CNKL4MMLBEPTLFJIQHLH5ETZMG","short_pith_number":"pith:CNKL4MML","schema_version":"1.0","canonical_sha256":"1354be318b091f35952881d67e9279619e757d2510061c51bb0d89fc73221ae7","source":{"kind":"arxiv","id":"2410.02479","version":1},"attestation_state":"computed","paper":{"title":"Cross-Embodiment Dexterous Grasping with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Bohan Zhou, Haoqi Yuan, Yuhui Fu, Zongqing Lu","submitted_at":"2024-10-03T13:36:02Z","abstract_excerpt":"Dexterous hands exhibit significant potential for complex real-world grasping tasks. While recent studies have primarily focused on learning policies for specific robotic hands, the development of a universal policy that controls diverse dexterous hands remains largely unexplored. In this work, we study the learning of cross-embodiment dexterous grasping policies using reinforcement learning (RL). Inspired by the capability of human hands to control various dexterous hands through teleoperation, we propose a universal action space based on the human hand's eigengrasps. The policy outputs eigen"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.02479","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-03T13:36:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9aedc820294790f206810ab447da078a23f04c56b52818155131ecb3e01e7007","abstract_canon_sha256":"c90f90e71eaec8dd1712967c8f8cacd60f4611f14760115c1bfd19a742727633"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:21.630172Z","signature_b64":"BtsS8SOjw6G5MXP/Rl92ydxW8vZFagYHZVRSuFuSQ8BnxZykrGgHJrZPG9OAZdzFuzx9K09mEvTcrH/FEXLSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1354be318b091f35952881d67e9279619e757d2510061c51bb0d89fc73221ae7","last_reissued_at":"2026-07-05T09:15:21.629687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:21.629687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-Embodiment Dexterous Grasping with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Bohan Zhou, Haoqi Yuan, Yuhui Fu, Zongqing Lu","submitted_at":"2024-10-03T13:36:02Z","abstract_excerpt":"Dexterous hands exhibit significant potential for complex real-world grasping tasks. While recent studies have primarily focused on learning policies for specific robotic hands, the development of a universal policy that controls diverse dexterous hands remains largely unexplored. In this work, we study the learning of cross-embodiment dexterous grasping policies using reinforcement learning (RL). Inspired by the capability of human hands to control various dexterous hands through teleoperation, we propose a universal action space based on the human hand's eigengrasps. The policy outputs eigen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02479","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.02479/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.02479","created_at":"2026-07-05T09:15:21.629746+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02479v1","created_at":"2026-07-05T09:15:21.629746+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02479","created_at":"2026-07-05T09:15:21.629746+00:00"},{"alias_kind":"pith_short_12","alias_value":"CNKL4MMLBEPT","created_at":"2026-07-05T09:15:21.629746+00:00"},{"alias_kind":"pith_short_16","alias_value":"CNKL4MMLBEPTLFJI","created_at":"2026-07-05T09:15:21.629746+00:00"},{"alias_kind":"pith_short_8","alias_value":"CNKL4MML","created_at":"2026-07-05T09:15:21.629746+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22113","citing_title":"KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.15516","citing_title":"Transferring Contact, Not Just Motion: Compliant Grasping Across Dexterous Hands","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16743","citing_title":"LACE: Latent Visual Representation for Cross-Embodiment Learning","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13925","citing_title":"Towards Robotic Dexterous Hand Intelligence: A Survey","ref_index":127,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13117","citing_title":"SECOND-Grasp: Semantic Contact-guided Dexterous Grasping","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG","json":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG.json","graph_json":"https://pith.science/api/pith-number/CNKL4MMLBEPTLFJIQHLH5ETZMG/graph.json","events_json":"https://pith.science/api/pith-number/CNKL4MMLBEPTLFJIQHLH5ETZMG/events.json","paper":"https://pith.science/paper/CNKL4MML"},"agent_actions":{"view_html":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG","download_json":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG.json","view_paper":"https://pith.science/paper/CNKL4MML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02479&json=true","fetch_graph":"https://pith.science/api/pith-number/CNKL4MMLBEPTLFJIQHLH5ETZMG/graph.json","fetch_events":"https://pith.science/api/pith-number/CNKL4MMLBEPTLFJIQHLH5ETZMG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG/action/storage_attestation","attest_author":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG/action/author_attestation","sign_citation":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG/action/citation_signature","submit_replication":"https://pith.science/pith/CNKL4MMLBEPTLFJIQHLH5ETZMG/action/replication_record"}},"created_at":"2026-07-05T09:15:21.629746+00:00","updated_at":"2026-07-05T09:15:21.629746+00:00"}