{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZKJK5UFU5SET3DWYPUBDBMMH4D","short_pith_number":"pith:ZKJK5UFU","schema_version":"1.0","canonical_sha256":"ca92aed0b4ec893d8ed87d0230b187e0de292877377841cdd344ce320886cb5f","source":{"kind":"arxiv","id":"2310.04582","version":2},"attestation_state":"computed","paper":{"title":"Universal Humanoid Motion Representations for Physics-Based Control","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.GR","cs.RO"],"primary_cat":"cs.CV","authors_text":"Alexander Winkler, Jing Huang, Jinkun Cao, Josh Merel, Kris Kitani, Weipeng Xu, Zhengyi Luo","submitted_at":"2023-10-06T20:48:43Z","abstract_excerpt":"We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game characters) from specialized motion datasets. This limited scope hampers their applicability in complex tasks. We close this gap by significantly increasing the coverage of our motion representation space. To achieve this, we first learn a motion"},"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":"2310.04582","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-06T20:48:43Z","cross_cats_sorted":["cs.GR","cs.RO"],"title_canon_sha256":"4f81bfb9c6aa3ed3195e277c3b976b6f206c79761a202d210c7ced73fd035a91","abstract_canon_sha256":"8b53934e028837a69901cf1854c478aa5ec340c313dd534f4032d414f49f07ab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:11.455185Z","signature_b64":"jTfkDQr9oIkIH/3UKPrIc6bjKU1TFKCk7qu4r3N7AwV//v5P2AHVjC40eJWCRDNvGcdZHGZH3Idou/SraYzQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca92aed0b4ec893d8ed87d0230b187e0de292877377841cdd344ce320886cb5f","last_reissued_at":"2026-07-05T08:07:11.454702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:11.454702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Universal Humanoid Motion Representations for Physics-Based Control","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.GR","cs.RO"],"primary_cat":"cs.CV","authors_text":"Alexander Winkler, Jing Huang, Jinkun Cao, Josh Merel, Kris Kitani, Weipeng Xu, Zhengyi Luo","submitted_at":"2023-10-06T20:48:43Z","abstract_excerpt":"We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game characters) from specialized motion datasets. This limited scope hampers their applicability in complex tasks. We close this gap by significantly increasing the coverage of our motion representation space. To achieve this, we first learn a motion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04582","kind":"arxiv","version":2},"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/2310.04582/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":"2310.04582","created_at":"2026-07-05T08:07:11.454759+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.04582v2","created_at":"2026-07-05T08:07:11.454759+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04582","created_at":"2026-07-05T08:07:11.454759+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKJK5UFU5SET","created_at":"2026-07-05T08:07:11.454759+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKJK5UFU5SET3DWY","created_at":"2026-07-05T08:07:11.454759+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKJK5UFU","created_at":"2026-07-05T08:07:11.454759+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2607.08741","citing_title":"ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2607.06438","citing_title":"WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation","ref_index":35,"is_internal_anchor":true},{"citing_arxiv_id":"2606.06953","citing_title":"LIMMT: Less is More for Motion Tracking","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03985","citing_title":"Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03536","citing_title":"Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2502.19056","citing_title":"Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2508.14098","citing_title":"No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21351","citing_title":"Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07984","citing_title":"Physics-Based Motion Tracking of Contact-Rich Interacting Characters","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18557","citing_title":"SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20336","citing_title":"Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D","json":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D.json","graph_json":"https://pith.science/api/pith-number/ZKJK5UFU5SET3DWYPUBDBMMH4D/graph.json","events_json":"https://pith.science/api/pith-number/ZKJK5UFU5SET3DWYPUBDBMMH4D/events.json","paper":"https://pith.science/paper/ZKJK5UFU"},"agent_actions":{"view_html":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D","download_json":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D.json","view_paper":"https://pith.science/paper/ZKJK5UFU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.04582&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKJK5UFU5SET3DWYPUBDBMMH4D/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKJK5UFU5SET3DWYPUBDBMMH4D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D/action/storage_attestation","attest_author":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D/action/author_attestation","sign_citation":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D/action/citation_signature","submit_replication":"https://pith.science/pith/ZKJK5UFU5SET3DWYPUBDBMMH4D/action/replication_record"}},"created_at":"2026-07-05T08:07:11.454759+00:00","updated_at":"2026-07-05T08:07:11.454759+00:00"}