{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XJBDDHZ7YD62K7RD6J6IAZBYIP","short_pith_number":"pith:XJBDDHZ7","schema_version":"1.0","canonical_sha256":"ba42319f3fc0fda57e23f27c80643843e06961c703b97f4e5b1e6068b67c1a4e","source":{"kind":"arxiv","id":"2511.01177","version":3},"attestation_state":"computed","paper":{"title":"Scaling Cross-Embodiment World Models for Dexterous Manipulation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bo Ai, Hao Su, Henrik I. Christensen, Jiawei Fu, Tongzhou Mu, Weikang Wan, Yilun Du, Yulin Liu, Zihao He","submitted_at":"2025-11-03T03:02:16Z","abstract_excerpt":"Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data sharing and control transfer. We ask: What structure can be shared across embodiments despite these differences? We argue that the physical interactions they induce can be modeled in a shared geometric space, allowing world models to provide a common interface for learning and control. To realize this idea, we represent human and robot hands as sets of 3D particles and define actions as end-effector particle displacement"},"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":"2511.01177","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2025-11-03T03:02:16Z","cross_cats_sorted":[],"title_canon_sha256":"b6facc3a15159bb1fad29ec71f41b3e3c2407c1a9711bf80e9063ee19bb4dfb2","abstract_canon_sha256":"13ae59b3ed8884bd8ddb0d93ecbc11eb097cab09f4c6241aaf1421b2657eb4eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:14.102795Z","signature_b64":"8VxyQjKoNUSPRgAVfDQULbrhhQyD1yxCcUUDx/l8qVWsz4u7tDH/yODqBS+qFUZWnTyD28jUYTb2g7v3/8FrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba42319f3fc0fda57e23f27c80643843e06961c703b97f4e5b1e6068b67c1a4e","last_reissued_at":"2026-07-22T00:22:14.101888Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:14.101888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Cross-Embodiment World Models for Dexterous Manipulation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bo Ai, Hao Su, Henrik I. Christensen, Jiawei Fu, Tongzhou Mu, Weikang Wan, Yilun Du, Yulin Liu, Zihao He","submitted_at":"2025-11-03T03:02:16Z","abstract_excerpt":"Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data sharing and control transfer. We ask: What structure can be shared across embodiments despite these differences? We argue that the physical interactions they induce can be modeled in a shared geometric space, allowing world models to provide a common interface for learning and control. To realize this idea, we represent human and robot hands as sets of 3D particles and define actions as end-effector particle displacement"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.01177","kind":"arxiv","version":3},"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/2511.01177/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":"2511.01177","created_at":"2026-07-22T00:22:14.102306+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.01177v3","created_at":"2026-07-22T00:22:14.102306+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.01177","created_at":"2026-07-22T00:22:14.102306+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJBDDHZ7YD62","created_at":"2026-07-22T00:22:14.102306+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJBDDHZ7YD62K7RD","created_at":"2026-07-22T00:22:14.102306+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJBDDHZ7","created_at":"2026-07-22T00:22:14.102306+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2602.16712","citing_title":"One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16743","citing_title":"LACE: Latent Visual Representation for Cross-Embodiment Learning","ref_index":50,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07607","citing_title":"EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2604.15483","citing_title":"${\\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities","ref_index":86,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP","json":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP.json","graph_json":"https://pith.science/api/pith-number/XJBDDHZ7YD62K7RD6J6IAZBYIP/graph.json","events_json":"https://pith.science/api/pith-number/XJBDDHZ7YD62K7RD6J6IAZBYIP/events.json","paper":"https://pith.science/paper/XJBDDHZ7"},"agent_actions":{"view_html":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP","download_json":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP.json","view_paper":"https://pith.science/paper/XJBDDHZ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.01177&json=true","fetch_graph":"https://pith.science/api/pith-number/XJBDDHZ7YD62K7RD6J6IAZBYIP/graph.json","fetch_events":"https://pith.science/api/pith-number/XJBDDHZ7YD62K7RD6J6IAZBYIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP/action/storage_attestation","attest_author":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP/action/author_attestation","sign_citation":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP/action/citation_signature","submit_replication":"https://pith.science/pith/XJBDDHZ7YD62K7RD6J6IAZBYIP/action/replication_record"}},"created_at":"2026-07-22T00:22:14.102306+00:00","updated_at":"2026-07-22T00:22:14.102306+00:00"}