{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5C2KZVW4M2DGVJ5MBVSJ745MGG","short_pith_number":"pith:5C2KZVW4","schema_version":"1.0","canonical_sha256":"e8b4acd6dc66866aa7ac0d649ff3ac31b0c39b63e6a12e744fe7ab390f705ba0","source":{"kind":"arxiv","id":"2309.06440","version":1},"attestation_state":"computed","paper":{"title":"LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Ananye Agarwal, Deepak Pathak, Kenneth Shaw","submitted_at":"2023-09-12T17:59:20Z","abstract_excerpt":"Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available part"},"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":"2309.06440","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-09-12T17:59:20Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"4a441e30d380e6abe7044883d868fb003f3c58097b7e9f784a7ff9aff0fd8fe5","abstract_canon_sha256":"b27460d5adb0a67cf8287331de67d75fcc2c84961d06d59fb7a724be0562acd6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:08.259849Z","signature_b64":"J4RqWxyTbg7rf0e9tRN1vbOjyLuj0JsKbsb8b/iyTmcmtzv/kwqWxTEqywlxSmsbpDf3v+USz/SsZnsrk8maBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8b4acd6dc66866aa7ac0d649ff3ac31b0c39b63e6a12e744fe7ab390f705ba0","last_reissued_at":"2026-07-05T06:50:08.259402Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:08.259402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Ananye Agarwal, Deepak Pathak, Kenneth Shaw","submitted_at":"2023-09-12T17:59:20Z","abstract_excerpt":"Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available part"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.06440","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/2309.06440/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":"2309.06440","created_at":"2026-07-05T06:50:08.259458+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.06440v1","created_at":"2026-07-05T06:50:08.259458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.06440","created_at":"2026-07-05T06:50:08.259458+00:00"},{"alias_kind":"pith_short_12","alias_value":"5C2KZVW4M2DG","created_at":"2026-07-05T06:50:08.259458+00:00"},{"alias_kind":"pith_short_16","alias_value":"5C2KZVW4M2DGVJ5M","created_at":"2026-07-05T06:50:08.259458+00:00"},{"alias_kind":"pith_short_8","alias_value":"5C2KZVW4","created_at":"2026-07-05T06:50:08.259458+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":16,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08751","citing_title":"DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation","ref_index":38,"is_internal_anchor":true},{"citing_arxiv_id":"2606.24450","citing_title":"NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20549","citing_title":"Generating Robot Hands from Human Demonstrations","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20193","citing_title":"Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.17418","citing_title":"DexLink Hand: A Compact, Affordable, 16-DOF Linkage-Driven Hand with Human-Like Dexterity","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11767","citing_title":"Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08828","citing_title":"Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08765","citing_title":"RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27475","citing_title":"Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30569","citing_title":"Any-ttach: Quick End-effector Swapping Enables Manipulation Dexterity with Simplicity","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2602.16712","citing_title":"One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2506.02618","citing_title":"Rodrigues Network for Learning Robot Actions","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27557","citing_title":"Function-based Parametric Co-Design Optimization of Dexterous Hands","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20689","citing_title":"FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06589","citing_title":"BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22499","citing_title":"Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG","json":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG.json","graph_json":"https://pith.science/api/pith-number/5C2KZVW4M2DGVJ5MBVSJ745MGG/graph.json","events_json":"https://pith.science/api/pith-number/5C2KZVW4M2DGVJ5MBVSJ745MGG/events.json","paper":"https://pith.science/paper/5C2KZVW4"},"agent_actions":{"view_html":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG","download_json":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG.json","view_paper":"https://pith.science/paper/5C2KZVW4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.06440&json=true","fetch_graph":"https://pith.science/api/pith-number/5C2KZVW4M2DGVJ5MBVSJ745MGG/graph.json","fetch_events":"https://pith.science/api/pith-number/5C2KZVW4M2DGVJ5MBVSJ745MGG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG/action/storage_attestation","attest_author":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG/action/author_attestation","sign_citation":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG/action/citation_signature","submit_replication":"https://pith.science/pith/5C2KZVW4M2DGVJ5MBVSJ745MGG/action/replication_record"}},"created_at":"2026-07-05T06:50:08.259458+00:00","updated_at":"2026-07-05T06:50:08.259458+00:00"}