{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GB32RU2RG2XTWOKIA7TSCPMWKG","short_pith_number":"pith:GB32RU2R","schema_version":"1.0","canonical_sha256":"3077a8d35136af3b394807e7213d9651ba2541815817b8d3991b0e32024ff0a8","source":{"kind":"arxiv","id":"2508.13534","version":1},"attestation_state":"computed","paper":{"title":"MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Anxing Xiao, Chao Tang, David Hsu, Hanbo Zhang, Hong Zhang, Tianrun Hu, Wenlong Dong, Yuhong Deng","submitted_at":"2025-08-19T05:49:47Z","abstract_excerpt":"Imitating tool manipulation from human videos offers an intuitive approach to teaching robots, while also providing a promising and scalable alternative to labor-intensive teleoperation data collection for visuomotor policy learning. While humans can mimic tool manipulation behavior by observing others perform a task just once and effortlessly transfer the skill to diverse tools for functionally equivalent tasks, current robots struggle to achieve this level of generalization. A key challenge lies in establishing function-level correspondences, considering the significant geometric variations "},"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":"2508.13534","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-08-19T05:49:47Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"a2896025cca8d65940f5f699912d21545f971629dd53ec662594d8f721c0098d","abstract_canon_sha256":"e06f519516f90371be06fb9044f890ce7856c86bbd40ab81c25fa21417341092"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:49.712367Z","signature_b64":"SAChm+ZMDKnuiFOSRqks9QyKwh9oeYeGMfevz8plHdIdeMjPPqlxnKSft0GS2OdE/j+zBg2ESiKayFpMW2YYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3077a8d35136af3b394807e7213d9651ba2541815817b8d3991b0e32024ff0a8","last_reissued_at":"2026-07-05T11:55:49.711762Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:49.711762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Anxing Xiao, Chao Tang, David Hsu, Hanbo Zhang, Hong Zhang, Tianrun Hu, Wenlong Dong, Yuhong Deng","submitted_at":"2025-08-19T05:49:47Z","abstract_excerpt":"Imitating tool manipulation from human videos offers an intuitive approach to teaching robots, while also providing a promising and scalable alternative to labor-intensive teleoperation data collection for visuomotor policy learning. While humans can mimic tool manipulation behavior by observing others perform a task just once and effortlessly transfer the skill to diverse tools for functionally equivalent tasks, current robots struggle to achieve this level of generalization. A key challenge lies in establishing function-level correspondences, considering the significant geometric variations "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.13534","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/2508.13534/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":"2508.13534","created_at":"2026-07-05T11:55:49.711831+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.13534v1","created_at":"2026-07-05T11:55:49.711831+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.13534","created_at":"2026-07-05T11:55:49.711831+00:00"},{"alias_kind":"pith_short_12","alias_value":"GB32RU2RG2XT","created_at":"2026-07-05T11:55:49.711831+00:00"},{"alias_kind":"pith_short_16","alias_value":"GB32RU2RG2XTWOKI","created_at":"2026-07-05T11:55:49.711831+00:00"},{"alias_kind":"pith_short_8","alias_value":"GB32RU2R","created_at":"2026-07-05T11:55:49.711831+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19340","citing_title":"ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13925","citing_title":"Towards Robotic Dexterous Hand Intelligence: A Survey","ref_index":163,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10579","citing_title":"AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15569","citing_title":"ShapeGen: Robotic Data Generation for Category-Level Manipulation","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG","json":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG.json","graph_json":"https://pith.science/api/pith-number/GB32RU2RG2XTWOKIA7TSCPMWKG/graph.json","events_json":"https://pith.science/api/pith-number/GB32RU2RG2XTWOKIA7TSCPMWKG/events.json","paper":"https://pith.science/paper/GB32RU2R"},"agent_actions":{"view_html":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG","download_json":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG.json","view_paper":"https://pith.science/paper/GB32RU2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.13534&json=true","fetch_graph":"https://pith.science/api/pith-number/GB32RU2RG2XTWOKIA7TSCPMWKG/graph.json","fetch_events":"https://pith.science/api/pith-number/GB32RU2RG2XTWOKIA7TSCPMWKG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG/action/storage_attestation","attest_author":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG/action/author_attestation","sign_citation":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG/action/citation_signature","submit_replication":"https://pith.science/pith/GB32RU2RG2XTWOKIA7TSCPMWKG/action/replication_record"}},"created_at":"2026-07-05T11:55:49.711831+00:00","updated_at":"2026-07-05T11:55:49.711831+00:00"}