{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7QCAJJG7OUP3OLW2KPZYPQWCTM","short_pith_number":"pith:7QCAJJG7","schema_version":"1.0","canonical_sha256":"fc0404a4df751fb72eda53f387c2c29b014d0d1318da66e8c484e1d6edf6162b","source":{"kind":"arxiv","id":"2410.09309","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Benjamin Burchfiel, Cheng Chi, Eric Cousineau, Naveen Kuppuswamy, Shuran Song, Siyuan Feng, Yifan Hou, Zeyi Liu","submitted_at":"2024-10-12T00:08:18Z","abstract_excerpt":"Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full "},"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":"2410.09309","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-12T00:08:18Z","cross_cats_sorted":[],"title_canon_sha256":"71150425192fb8eb7376e4af47f0d8febf742ec17ec600e6a5b42163aadc417d","abstract_canon_sha256":"8ca24fb8a11f56b48294732cdd6b5a6ed8f408d3f170dd9524667fde12fe130c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:08.802546Z","signature_b64":"jX/0GNaUTxu3wsdB0YoW1TH4Zcqda29vZOwKRL3nLbAiExHEt6EXZxS40cOUzR2GgHB/mo7ppcsuIlSnZ2WYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc0404a4df751fb72eda53f387c2c29b014d0d1318da66e8c484e1d6edf6162b","last_reissued_at":"2026-07-05T10:26:08.801737Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:08.801737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Benjamin Burchfiel, Cheng Chi, Eric Cousineau, Naveen Kuppuswamy, Shuran Song, Siyuan Feng, Yifan Hou, Zeyi Liu","submitted_at":"2024-10-12T00:08:18Z","abstract_excerpt":"Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09309","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/2410.09309/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":"2410.09309","created_at":"2026-07-05T10:26:08.801829+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.09309v2","created_at":"2026-07-05T10:26:08.801829+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09309","created_at":"2026-07-05T10:26:08.801829+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QCAJJG7OUP3","created_at":"2026-07-05T10:26:08.801829+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QCAJJG7OUP3OLW2","created_at":"2026-07-05T10:26:08.801829+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QCAJJG7","created_at":"2026-07-05T10:26:08.801829+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.31321","citing_title":"Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2510.02738","citing_title":"Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10647","citing_title":"OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22551","citing_title":"QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM","json":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM.json","graph_json":"https://pith.science/api/pith-number/7QCAJJG7OUP3OLW2KPZYPQWCTM/graph.json","events_json":"https://pith.science/api/pith-number/7QCAJJG7OUP3OLW2KPZYPQWCTM/events.json","paper":"https://pith.science/paper/7QCAJJG7"},"agent_actions":{"view_html":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM","download_json":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM.json","view_paper":"https://pith.science/paper/7QCAJJG7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.09309&json=true","fetch_graph":"https://pith.science/api/pith-number/7QCAJJG7OUP3OLW2KPZYPQWCTM/graph.json","fetch_events":"https://pith.science/api/pith-number/7QCAJJG7OUP3OLW2KPZYPQWCTM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM/action/storage_attestation","attest_author":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM/action/author_attestation","sign_citation":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM/action/citation_signature","submit_replication":"https://pith.science/pith/7QCAJJG7OUP3OLW2KPZYPQWCTM/action/replication_record"}},"created_at":"2026-07-05T10:26:08.801829+00:00","updated_at":"2026-07-05T10:26:08.801829+00:00"}