{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2CWGBCYNHGUXU4JJWBDLBFP3YX","short_pith_number":"pith:2CWGBCYN","schema_version":"1.0","canonical_sha256":"d0ac608b0d39a97a7129b046b095fbc5c39efb8169be01bb99ca1c05f595bd00","source":{"kind":"arxiv","id":"2407.02274","version":3},"attestation_state":"computed","paper":{"title":"DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ankur Handa, Arthur Allshire, Karl Van Wyk, Martin Matak, Nathan D. Ratliff, Tucker Hermans, Tyler Ga Wei Lum, Viktor Makoviychuk","submitted_at":"2024-07-02T14:03:49Z","abstract_excerpt":"A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However, existing methods often have very limited speed, dexterity, and generality, along with limited or no hardware safety guarantees. In this work, we introduce DextrAH-G, a depth-based dexterous grasping policy trained entirely in simulation that combines reinforcement learning, geometric fabrics, and teacher-student distillation. We address key challenges in joint arm-hand policy learning, such as high-dimensional obser"},"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":"2407.02274","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-07-02T14:03:49Z","cross_cats_sorted":[],"title_canon_sha256":"01a8dd88e9e0c49b72bf0876640f269f7da35df939d9e8527875a726fceab9d4","abstract_canon_sha256":"062074e45ffd4af145c39c89026903077752a4e3b1e77dfce02b4bc549fe07dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:45.050295Z","signature_b64":"o6uUCfQx7/o/2QHnkTN31IPnHjN67fy1VNe4j46va73XNI5x6Y8nedMj81JBF8076nn9Ww1umHAw9z5FZVCaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0ac608b0d39a97a7129b046b095fbc5c39efb8169be01bb99ca1c05f595bd00","last_reissued_at":"2026-07-05T09:20:45.049746Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:45.049746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ankur Handa, Arthur Allshire, Karl Van Wyk, Martin Matak, Nathan D. Ratliff, Tucker Hermans, Tyler Ga Wei Lum, Viktor Makoviychuk","submitted_at":"2024-07-02T14:03:49Z","abstract_excerpt":"A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However, existing methods often have very limited speed, dexterity, and generality, along with limited or no hardware safety guarantees. In this work, we introduce DextrAH-G, a depth-based dexterous grasping policy trained entirely in simulation that combines reinforcement learning, geometric fabrics, and teacher-student distillation. We address key challenges in joint arm-hand policy learning, such as high-dimensional obser"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02274","kind":"arxiv","version":3},"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/2407.02274/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":"2407.02274","created_at":"2026-07-05T09:20:45.049816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.02274v3","created_at":"2026-07-05T09:20:45.049816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02274","created_at":"2026-07-05T09:20:45.049816+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CWGBCYNHGUX","created_at":"2026-07-05T09:20:45.049816+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CWGBCYNHGUXU4JJ","created_at":"2026-07-05T09:20:45.049816+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CWGBCYN","created_at":"2026-07-05T09:20:45.049816+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22471","citing_title":"Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13677","citing_title":"Mana: Dexterous Manipulation of Articulated Tools","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03268","citing_title":"EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09989","citing_title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","ref_index":96,"is_internal_anchor":false},{"citing_arxiv_id":"2606.22471","citing_title":"Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26478","citing_title":"Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2510.03599","citing_title":"Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13925","citing_title":"Towards Robotic Dexterous Hand Intelligence: A Survey","ref_index":108,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03363","citing_title":"Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09989","citing_title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2511.04831","citing_title":"Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX","json":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX.json","graph_json":"https://pith.science/api/pith-number/2CWGBCYNHGUXU4JJWBDLBFP3YX/graph.json","events_json":"https://pith.science/api/pith-number/2CWGBCYNHGUXU4JJWBDLBFP3YX/events.json","paper":"https://pith.science/paper/2CWGBCYN"},"agent_actions":{"view_html":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX","download_json":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX.json","view_paper":"https://pith.science/paper/2CWGBCYN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.02274&json=true","fetch_graph":"https://pith.science/api/pith-number/2CWGBCYNHGUXU4JJWBDLBFP3YX/graph.json","fetch_events":"https://pith.science/api/pith-number/2CWGBCYNHGUXU4JJWBDLBFP3YX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX/action/storage_attestation","attest_author":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX/action/author_attestation","sign_citation":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX/action/citation_signature","submit_replication":"https://pith.science/pith/2CWGBCYNHGUXU4JJWBDLBFP3YX/action/replication_record"}},"created_at":"2026-07-05T09:20:45.049816+00:00","updated_at":"2026-07-05T09:20:45.049816+00:00"}