{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YTYALRFZMRBSN7GYYT4CQHBLSC","short_pith_number":"pith:YTYALRFZ","schema_version":"1.0","canonical_sha256":"c4f005c4b9644326fcd8c4f8281c2b90a2812d4cf0382f77032bfd3ad248b494","source":{"kind":"arxiv","id":"2404.16823","version":2},"attestation_state":"computed","paper":{"title":"Learning Visuotactile Skills with Two Multifingered Hands","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Brent Yi, Haozhi Qi, Jitendra Malik, Qiyang Li, Sergey Levine, Toru Lin, Yu Zhang","submitted_at":"2024-04-25T17:59:41Z","abstract_excerpt":"Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing. To tackle the first challenge, we develop HATO, a low-cost hands-arms teleoperation system that leverages off-the-shelf electronics, complemented with a software suit"},"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":"2404.16823","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-04-25T17:59:41Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"3e52169b9d637ad1a55ba676bab63e4f1676c48373e5258434bb669ff5301e75","abstract_canon_sha256":"a1657999be073389d7cebffcb8d259d496db47e4e1649f8f278b6a33fa433fd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:53.084098Z","signature_b64":"6tBGlK6w05WhFLsmNpBTJoGqorxdNLOoILRuTCdw5Oe0KO1WPa0z1t93rHnx83fvqjwd/j6GBYBrHjatnF8cCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4f005c4b9644326fcd8c4f8281c2b90a2812d4cf0382f77032bfd3ad248b494","last_reissued_at":"2026-07-05T08:21:53.083626Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:53.083626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Visuotactile Skills with Two Multifingered Hands","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Brent Yi, Haozhi Qi, Jitendra Malik, Qiyang Li, Sergey Levine, Toru Lin, Yu Zhang","submitted_at":"2024-04-25T17:59:41Z","abstract_excerpt":"Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing. To tackle the first challenge, we develop HATO, a low-cost hands-arms teleoperation system that leverages off-the-shelf electronics, complemented with a software suit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16823","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/2404.16823/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":"2404.16823","created_at":"2026-07-05T08:21:53.083690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16823v2","created_at":"2026-07-05T08:21:53.083690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16823","created_at":"2026-07-05T08:21:53.083690+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTYALRFZMRBS","created_at":"2026-07-05T08:21:53.083690+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTYALRFZMRBSN7GY","created_at":"2026-07-05T08:21:53.083690+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTYALRFZ","created_at":"2026-07-05T08:21:53.083690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":14,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26423","citing_title":"CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2606.17055","citing_title":"T-Rex: Tactile-Reactive Dexterous Manipulation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26423","citing_title":"CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30749","citing_title":"From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31909","citing_title":"CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31236","citing_title":"TactX: Learning Shared Tactile Representations Across Diverse Sensors","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29298","citing_title":"MonoDuo: Using One Robot Arm to Learn Bimanual Policies","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2410.14022","citing_title":"Language Conditioned Multi-Finger Dexterous Manipulation Enabled by Physical Compliance and Switching of Controllers","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2511.04812","citing_title":"Multimodal Diffusion Forcing for Forceful Manipulation","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2602.09628","citing_title":"TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2502.05855","citing_title":"DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.28156","citing_title":"FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21331","citing_title":"FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2501.09747","citing_title":"FAST: Efficient Action Tokenization for Vision-Language-Action Models","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC","json":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC.json","graph_json":"https://pith.science/api/pith-number/YTYALRFZMRBSN7GYYT4CQHBLSC/graph.json","events_json":"https://pith.science/api/pith-number/YTYALRFZMRBSN7GYYT4CQHBLSC/events.json","paper":"https://pith.science/paper/YTYALRFZ"},"agent_actions":{"view_html":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC","download_json":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC.json","view_paper":"https://pith.science/paper/YTYALRFZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16823&json=true","fetch_graph":"https://pith.science/api/pith-number/YTYALRFZMRBSN7GYYT4CQHBLSC/graph.json","fetch_events":"https://pith.science/api/pith-number/YTYALRFZMRBSN7GYYT4CQHBLSC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC/action/storage_attestation","attest_author":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC/action/author_attestation","sign_citation":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC/action/citation_signature","submit_replication":"https://pith.science/pith/YTYALRFZMRBSN7GYYT4CQHBLSC/action/replication_record"}},"created_at":"2026-07-05T08:21:53.083690+00:00","updated_at":"2026-07-05T08:21:53.083690+00:00"}