{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YBMURYOIPRAFIK677HL72CXWNJ","short_pith_number":"pith:YBMURYOI","schema_version":"1.0","canonical_sha256":"c05948e1c87c40542bdff9d7fd0af66a46a91f845847e3df580bccc0eb8f11fe","source":{"kind":"arxiv","id":"2603.09956","version":2},"attestation_state":"computed","paper":{"title":"Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Maegan Tucker, Steven Haener, Varun Madabushi, Xiaoyu Zhang","submitted_at":"2026-03-10T17:51:17Z","abstract_excerpt":"We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a multi-contact, whole-body trajectory optimization problem. Conventional kinematics-based retargeting methods rely solely on spatial motion capture (MoCap) data, inevitably introducing physically inconsistent artifacts, such as foot sliding and ground penetration, that severely degrade the performance of downstream imitation learning policies. To bridge this gap, KDMR extends beyond pure kinematics by explicitly enforcing rigid-body dynamics and conta"},"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":"2603.09956","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-03-10T17:51:17Z","cross_cats_sorted":[],"title_canon_sha256":"2772f51978b6b7f0576e27759f785fa8ffa349b9bb57fcbcdec56ea4ee9bb9ca","abstract_canon_sha256":"9184da2004114abd58dd17d957c6e70cffb8acc279b3db0bb44975dd03d86bc6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-03T00:10:35.215232Z","signature_b64":"I/d0Yo5Z6E95GKf8tBa+kPAgRJ86DlnGIV41clqw1sA6FQYTluxvfmqzoHrJOQYIoTSS679dOJ80DUGNr5MeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c05948e1c87c40542bdff9d7fd0af66a46a91f845847e3df580bccc0eb8f11fe","last_reissued_at":"2026-08-03T00:10:35.212924Z","signature_status":"signed_v1","first_computed_at":"2026-08-03T00:10:35.212924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Maegan Tucker, Steven Haener, Varun Madabushi, Xiaoyu Zhang","submitted_at":"2026-03-10T17:51:17Z","abstract_excerpt":"We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a multi-contact, whole-body trajectory optimization problem. Conventional kinematics-based retargeting methods rely solely on spatial motion capture (MoCap) data, inevitably introducing physically inconsistent artifacts, such as foot sliding and ground penetration, that severely degrade the performance of downstream imitation learning policies. To bridge this gap, KDMR extends beyond pure kinematics by explicitly enforcing rigid-body dynamics and conta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.09956","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/2603.09956/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":"2603.09956","created_at":"2026-08-03T00:10:35.214386+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.09956v2","created_at":"2026-08-03T00:10:35.214386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.09956","created_at":"2026-08-03T00:10:35.214386+00:00"},{"alias_kind":"pith_short_12","alias_value":"YBMURYOIPRAF","created_at":"2026-08-03T00:10:35.214386+00:00"},{"alias_kind":"pith_short_16","alias_value":"YBMURYOIPRAFIK67","created_at":"2026-08-03T00:10:35.214386+00:00"},{"alias_kind":"pith_short_8","alias_value":"YBMURYOI","created_at":"2026-08-03T00:10:35.214386+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.27813","citing_title":"Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ","json":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ.json","graph_json":"https://pith.science/api/pith-number/YBMURYOIPRAFIK677HL72CXWNJ/graph.json","events_json":"https://pith.science/api/pith-number/YBMURYOIPRAFIK677HL72CXWNJ/events.json","paper":"https://pith.science/paper/YBMURYOI"},"agent_actions":{"view_html":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ","download_json":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ.json","view_paper":"https://pith.science/paper/YBMURYOI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.09956&json=true","fetch_graph":"https://pith.science/api/pith-number/YBMURYOIPRAFIK677HL72CXWNJ/graph.json","fetch_events":"https://pith.science/api/pith-number/YBMURYOIPRAFIK677HL72CXWNJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ/action/storage_attestation","attest_author":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ/action/author_attestation","sign_citation":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ/action/citation_signature","submit_replication":"https://pith.science/pith/YBMURYOIPRAFIK677HL72CXWNJ/action/replication_record"}},"created_at":"2026-08-03T00:10:35.214386+00:00","updated_at":"2026-08-03T00:10:35.214386+00:00"}