{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QP3JWNGVLTFO3PGBLCTX5JEUAH","short_pith_number":"pith:QP3JWNGV","schema_version":"1.0","canonical_sha256":"83f69b34d55ccaedbcc158a77ea49401e888899b316a999d8ee2c4ce4205955f","source":{"kind":"arxiv","id":"2605.27724","version":1},"attestation_state":"computed","paper":{"title":"HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Ajay Mandlekar, Caelan Reed Garrett, Justin Tran, Kevin Lin, Linxi Fan, Nikita Chernyadev, Runyu Ding, Yu Fang, Yuke Zhu, Yuqi Xie","submitted_at":"2026-05-26T21:57:11Z","abstract_excerpt":"Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and difficult to collect via teleoperation. Existing data-generation algorithms can automatically synthesize demonstrations for manipulators, but they are ineffective on humanoids because their high-dimensional composite action spaces involve arms, legs, and torsos. We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data. Our method adapts contact-rich whole-body skills from a handful "},"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":"2605.27724","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-26T21:57:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e8f95b75cb9acefe005483594da785503aee815a65928b3bdbebfa170129dce","abstract_canon_sha256":"6e24fd4aecfa459c918917f46ae04110b097b3ed74af1d5fc096aea5df62450f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:47.251599Z","signature_b64":"KPA2t+mEA7bpG0rvG1xkcKnKto6QSl9T9EGsQEmM+BXa4NUJBuevFQveyJj6dWoOyUxsFRYENqtpZbs9fEjTCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83f69b34d55ccaedbcc158a77ea49401e888899b316a999d8ee2c4ce4205955f","last_reissued_at":"2026-05-28T01:04:47.251237Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:47.251237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Ajay Mandlekar, Caelan Reed Garrett, Justin Tran, Kevin Lin, Linxi Fan, Nikita Chernyadev, Runyu Ding, Yu Fang, Yuke Zhu, Yuqi Xie","submitted_at":"2026-05-26T21:57:11Z","abstract_excerpt":"Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and difficult to collect via teleoperation. Existing data-generation algorithms can automatically synthesize demonstrations for manipulators, but they are ineffective on humanoids because their high-dimensional composite action spaces involve arms, legs, and torsos. We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data. Our method adapts contact-rich whole-body skills from a handful "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27724","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/2605.27724/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":"2605.27724","created_at":"2026-05-28T01:04:47.251288+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27724v1","created_at":"2026-05-28T01:04:47.251288+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27724","created_at":"2026-05-28T01:04:47.251288+00:00"},{"alias_kind":"pith_short_12","alias_value":"QP3JWNGVLTFO","created_at":"2026-05-28T01:04:47.251288+00:00"},{"alias_kind":"pith_short_16","alias_value":"QP3JWNGVLTFO3PGB","created_at":"2026-05-28T01:04:47.251288+00:00"},{"alias_kind":"pith_short_8","alias_value":"QP3JWNGV","created_at":"2026-05-28T01:04:47.251288+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH","json":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH.json","graph_json":"https://pith.science/api/pith-number/QP3JWNGVLTFO3PGBLCTX5JEUAH/graph.json","events_json":"https://pith.science/api/pith-number/QP3JWNGVLTFO3PGBLCTX5JEUAH/events.json","paper":"https://pith.science/paper/QP3JWNGV"},"agent_actions":{"view_html":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH","download_json":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH.json","view_paper":"https://pith.science/paper/QP3JWNGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27724&json=true","fetch_graph":"https://pith.science/api/pith-number/QP3JWNGVLTFO3PGBLCTX5JEUAH/graph.json","fetch_events":"https://pith.science/api/pith-number/QP3JWNGVLTFO3PGBLCTX5JEUAH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH/action/storage_attestation","attest_author":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH/action/author_attestation","sign_citation":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH/action/citation_signature","submit_replication":"https://pith.science/pith/QP3JWNGVLTFO3PGBLCTX5JEUAH/action/replication_record"}},"created_at":"2026-05-28T01:04:47.251288+00:00","updated_at":"2026-05-28T01:04:47.251288+00:00"}