{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UYBUTX6BYHOZ4LH3ILK3JYSQKE","short_pith_number":"pith:UYBUTX6B","schema_version":"1.0","canonical_sha256":"a60349dfc1c1dd9e2cfb42d5b4e250511d25baf617262236d939b622ff5aabc2","source":{"kind":"arxiv","id":"2502.17432","version":2},"attestation_state":"computed","paper":{"title":"FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Deepak Pathak, Jason Jingzhou Liu, Kenneth Shaw, Ruslan Salakhutdinov, Tony Tao, Yulong Li","submitted_at":"2025-02-24T18:59:07Z","abstract_excerpt":"Many contact-rich tasks humans perform, such as box pickup or rolling dough, rely on force feedback for reliable execution. However, this force information, which is readily available in most robot arms, is not commonly used in teleoperation and policy learning. Consequently, robot behavior is often limited to quasi-static kinematic tasks that do not require intricate force-feedback. In this paper, we first present a low-cost, intuitive, bilateral teleoperation setup that relays external forces of the follower arm back to the teacher arm, facilitating data collection for complex, contact-rich "},"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":"2502.17432","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-24T18:59:07Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"3473b1627ced70f12f5d5a13d77648a30f42bcb315f4a5f15a83aefcac1cc30e","abstract_canon_sha256":"bdddc188059a1ae052ec38e8278a441b1485cd173d91fed2eb4908965f0e69a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:52.651488Z","signature_b64":"4z66e1zR19ky0j7Ozzb2T1XVDdiIUTVjvhbYT2tyUfWc1GZPXz5czICI5VDeoppwCXDnOmTeSSTN4GWQNqTbAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a60349dfc1c1dd9e2cfb42d5b4e250511d25baf617262236d939b622ff5aabc2","last_reissued_at":"2026-07-05T10:53:52.650926Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:52.650926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Deepak Pathak, Jason Jingzhou Liu, Kenneth Shaw, Ruslan Salakhutdinov, Tony Tao, Yulong Li","submitted_at":"2025-02-24T18:59:07Z","abstract_excerpt":"Many contact-rich tasks humans perform, such as box pickup or rolling dough, rely on force feedback for reliable execution. However, this force information, which is readily available in most robot arms, is not commonly used in teleoperation and policy learning. Consequently, robot behavior is often limited to quasi-static kinematic tasks that do not require intricate force-feedback. In this paper, we first present a low-cost, intuitive, bilateral teleoperation setup that relays external forces of the follower arm back to the teacher arm, facilitating data collection for complex, contact-rich "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17432","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/2502.17432/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":"2502.17432","created_at":"2026-07-05T10:53:52.650985+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.17432v2","created_at":"2026-07-05T10:53:52.650985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17432","created_at":"2026-07-05T10:53:52.650985+00:00"},{"alias_kind":"pith_short_12","alias_value":"UYBUTX6BYHOZ","created_at":"2026-07-05T10:53:52.650985+00:00"},{"alias_kind":"pith_short_16","alias_value":"UYBUTX6BYHOZ4LH3","created_at":"2026-07-05T10:53:52.650985+00:00"},{"alias_kind":"pith_short_8","alias_value":"UYBUTX6B","created_at":"2026-07-05T10:53:52.650985+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":12,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27375","citing_title":"Scalable Behavior Cloning with Open Data, Training, and Evaluation","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10818","citing_title":"IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06461","citing_title":"Flow-based Policy Adaptation without Policy Updates","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29941","citing_title":"Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21976","citing_title":"TacO: Benchmarking Tactile Sensors for Object Manipulation","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16257","citing_title":"DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo","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":9,"is_internal_anchor":false},{"citing_arxiv_id":"2512.07371","citing_title":"ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2602.13833","citing_title":"Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02600","citing_title":"CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16850","citing_title":"Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02600","citing_title":"CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE","json":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE.json","graph_json":"https://pith.science/api/pith-number/UYBUTX6BYHOZ4LH3ILK3JYSQKE/graph.json","events_json":"https://pith.science/api/pith-number/UYBUTX6BYHOZ4LH3ILK3JYSQKE/events.json","paper":"https://pith.science/paper/UYBUTX6B"},"agent_actions":{"view_html":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE","download_json":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE.json","view_paper":"https://pith.science/paper/UYBUTX6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.17432&json=true","fetch_graph":"https://pith.science/api/pith-number/UYBUTX6BYHOZ4LH3ILK3JYSQKE/graph.json","fetch_events":"https://pith.science/api/pith-number/UYBUTX6BYHOZ4LH3ILK3JYSQKE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE/action/storage_attestation","attest_author":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE/action/author_attestation","sign_citation":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE/action/citation_signature","submit_replication":"https://pith.science/pith/UYBUTX6BYHOZ4LH3ILK3JYSQKE/action/replication_record"}},"created_at":"2026-07-05T10:53:52.650985+00:00","updated_at":"2026-07-05T10:53:52.650985+00:00"}