{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L2E744YYIPLGX2D55YAXAHEUU4","short_pith_number":"pith:L2E744YY","schema_version":"1.0","canonical_sha256":"5e89fe731843d66be87dee01701c94a715d552b103902ad83e5176fd5c9fde05","source":{"kind":"arxiv","id":"2504.05330","version":1},"attestation_state":"computed","paper":{"title":"Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bo Lu, Madaoji Ban, Peng Qi, Tianliang Yao, Zhiqiang Pei","submitted_at":"2025-04-04T09:45:41Z","abstract_excerpt":"Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing d"},"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":"2504.05330","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-04-04T09:45:41Z","cross_cats_sorted":[],"title_canon_sha256":"43d8181b0071195fe157d8d8fb2d94ef57816585fb3b8aa5514ec6c2a06c4450","abstract_canon_sha256":"1d9f188c4b1809660d3597e986e10d0acfb35c09052cc2f9b1d21cfa7c5eed00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:40.561136Z","signature_b64":"ieGyGAx8eSYTdEUWvcodKRqjGWfye5lHzWTSGpd2kv9t6q2JcpbzvN6nWOp0aLo6jQruo4Zix60ahEZBYTFaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e89fe731843d66be87dee01701c94a715d552b103902ad83e5176fd5c9fde05","last_reissued_at":"2026-07-05T10:45:40.560511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:40.560511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bo Lu, Madaoji Ban, Peng Qi, Tianliang Yao, Zhiqiang Pei","submitted_at":"2025-04-04T09:45:41Z","abstract_excerpt":"Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05330","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/2504.05330/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":"2504.05330","created_at":"2026-07-05T10:45:40.560583+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.05330v1","created_at":"2026-07-05T10:45:40.560583+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05330","created_at":"2026-07-05T10:45:40.560583+00:00"},{"alias_kind":"pith_short_12","alias_value":"L2E744YYIPLG","created_at":"2026-07-05T10:45:40.560583+00:00"},{"alias_kind":"pith_short_16","alias_value":"L2E744YYIPLGX2D5","created_at":"2026-07-05T10:45:40.560583+00:00"},{"alias_kind":"pith_short_8","alias_value":"L2E744YY","created_at":"2026-07-05T10:45:40.560583+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21631","citing_title":"Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4","json":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4.json","graph_json":"https://pith.science/api/pith-number/L2E744YYIPLGX2D55YAXAHEUU4/graph.json","events_json":"https://pith.science/api/pith-number/L2E744YYIPLGX2D55YAXAHEUU4/events.json","paper":"https://pith.science/paper/L2E744YY"},"agent_actions":{"view_html":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4","download_json":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4.json","view_paper":"https://pith.science/paper/L2E744YY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.05330&json=true","fetch_graph":"https://pith.science/api/pith-number/L2E744YYIPLGX2D55YAXAHEUU4/graph.json","fetch_events":"https://pith.science/api/pith-number/L2E744YYIPLGX2D55YAXAHEUU4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4/action/storage_attestation","attest_author":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4/action/author_attestation","sign_citation":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4/action/citation_signature","submit_replication":"https://pith.science/pith/L2E744YYIPLGX2D55YAXAHEUU4/action/replication_record"}},"created_at":"2026-07-05T10:45:40.560583+00:00","updated_at":"2026-07-05T10:45:40.560583+00:00"}