{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NPUO6LH53Y6SIIY6237XBJSZL7","short_pith_number":"pith:NPUO6LH5","schema_version":"1.0","canonical_sha256":"6be8ef2cfdde3d24231ed6ff70a6595feb19bb41905cd75a952bd701a3fd540e","source":{"kind":"arxiv","id":"2404.16027","version":1},"attestation_state":"computed","paper":{"title":"ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Animesh Garg, Huang Huang, Jingzhou Liu, Karthik Dharmarajan, Ken Goldberg, Kush Hari, Masoud Moghani, Mayank Mittal, Qinxi Yu, Vincent Schorp, William Chung-Ho Panitch","submitted_at":"2024-04-24T17:57:18Z","abstract_excerpt":"Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment remains a challenge. In this paper, we present ORBIT-Surgical, a physics-based surgical robot simulation framework with photorealistic rendering in NVIDIA Omniverse. We provide 14 benchmark surgical tasks for the da Vinci Research Kit (dVRK) and Smart Tissue Autonomous Robot (STAR) which represent common subtasks in surgical training. ORBIT-Surgical leverages GPU parallelization to train reinforcement learning and imi"},"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":"2404.16027","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-04-24T17:57:18Z","cross_cats_sorted":[],"title_canon_sha256":"bd8e10cb040795d6f1831c370664ad213ae838ebc9138cfd20cae4390aaf3797","abstract_canon_sha256":"e9d18b1299ddc64e4e9cf79e7abe9ffffd825c7e16a63c4618db492b38add073"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:48.460913Z","signature_b64":"DRrDMi1IEqBhAWLNhbBBlSCI7hSYiM03RNmE7vEbK3cwuhhtpqo2SpYtpY/grbVt1TAZsst1Tg+RK6TuXI+CBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6be8ef2cfdde3d24231ed6ff70a6595feb19bb41905cd75a952bd701a3fd540e","last_reissued_at":"2026-07-05T08:11:48.460460Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:48.460460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Animesh Garg, Huang Huang, Jingzhou Liu, Karthik Dharmarajan, Ken Goldberg, Kush Hari, Masoud Moghani, Mayank Mittal, Qinxi Yu, Vincent Schorp, William Chung-Ho Panitch","submitted_at":"2024-04-24T17:57:18Z","abstract_excerpt":"Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment remains a challenge. In this paper, we present ORBIT-Surgical, a physics-based surgical robot simulation framework with photorealistic rendering in NVIDIA Omniverse. We provide 14 benchmark surgical tasks for the da Vinci Research Kit (dVRK) and Smart Tissue Autonomous Robot (STAR) which represent common subtasks in surgical training. ORBIT-Surgical leverages GPU parallelization to train reinforcement learning and imi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16027","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/2404.16027/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":"2404.16027","created_at":"2026-07-05T08:11:48.460517+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16027v1","created_at":"2026-07-05T08:11:48.460517+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16027","created_at":"2026-07-05T08:11:48.460517+00:00"},{"alias_kind":"pith_short_12","alias_value":"NPUO6LH53Y6S","created_at":"2026-07-05T08:11:48.460517+00:00"},{"alias_kind":"pith_short_16","alias_value":"NPUO6LH53Y6SIIY6","created_at":"2026-07-05T08:11:48.460517+00:00"},{"alias_kind":"pith_short_8","alias_value":"NPUO6LH5","created_at":"2026-07-05T08:11:48.460517+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28736","citing_title":"Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7","json":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7.json","graph_json":"https://pith.science/api/pith-number/NPUO6LH53Y6SIIY6237XBJSZL7/graph.json","events_json":"https://pith.science/api/pith-number/NPUO6LH53Y6SIIY6237XBJSZL7/events.json","paper":"https://pith.science/paper/NPUO6LH5"},"agent_actions":{"view_html":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7","download_json":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7.json","view_paper":"https://pith.science/paper/NPUO6LH5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16027&json=true","fetch_graph":"https://pith.science/api/pith-number/NPUO6LH53Y6SIIY6237XBJSZL7/graph.json","fetch_events":"https://pith.science/api/pith-number/NPUO6LH53Y6SIIY6237XBJSZL7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7/action/storage_attestation","attest_author":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7/action/author_attestation","sign_citation":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7/action/citation_signature","submit_replication":"https://pith.science/pith/NPUO6LH53Y6SIIY6237XBJSZL7/action/replication_record"}},"created_at":"2026-07-05T08:11:48.460517+00:00","updated_at":"2026-07-05T08:11:48.460517+00:00"}