{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7ZWU2YG7ZVAWF5NJI3DN6OQDSX","short_pith_number":"pith:7ZWU2YG7","schema_version":"1.0","canonical_sha256":"fe6d4d60dfcd4162f5a946c6df3a0395c223eb46081db0d2aeafac8ea370bb11","source":{"kind":"arxiv","id":"2502.03072","version":1},"attestation_state":"computed","paper":{"title":"RoboGrasp: A Universal Grasping Policy for Robust Robotic Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Jiahuan Yan, Luhui Hu, Travis Davies, Xiang Chen, Yiqi Huang, Yu Tian","submitted_at":"2025-02-05T11:04:41Z","abstract_excerpt":"Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on robot arm state data and RGB images, leading to overfitting to specific object shapes or positions. To address these limitations, we propose RoboGrasp, a universal grasping policy framework that integrates pretrained grasp detection models with robotic learning. By leveraging robust visual guidance from object detection and segmentation tasks, RoboGrasp sign"},"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":"2502.03072","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-05T11:04:41Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e77d25cd011f370b1209c25874d4c43c1c866267abf595cf7e994597dc3acf52","abstract_canon_sha256":"2b3f3cf9d3ed62d4445b8129a1496ea0972a6bf4460016959701fb8a53b2fcc6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:59.196655Z","signature_b64":"PNxZM2BR6AYeFEWPkCeC80kYPGAuTvXAGx9LYOZ8MA2EiYyEbNoYM4O3v3lalaG+SPJN/Mn+6mpgVJ//UmSRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe6d4d60dfcd4162f5a946c6df3a0395c223eb46081db0d2aeafac8ea370bb11","last_reissued_at":"2026-07-05T10:09:59.196156Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:59.196156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RoboGrasp: A Universal Grasping Policy for Robust Robotic Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Jiahuan Yan, Luhui Hu, Travis Davies, Xiang Chen, Yiqi Huang, Yu Tian","submitted_at":"2025-02-05T11:04:41Z","abstract_excerpt":"Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on robot arm state data and RGB images, leading to overfitting to specific object shapes or positions. To address these limitations, we propose RoboGrasp, a universal grasping policy framework that integrates pretrained grasp detection models with robotic learning. By leveraging robust visual guidance from object detection and segmentation tasks, RoboGrasp sign"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03072","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/2502.03072/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":"2502.03072","created_at":"2026-07-05T10:09:59.196216+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.03072v1","created_at":"2026-07-05T10:09:59.196216+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03072","created_at":"2026-07-05T10:09:59.196216+00:00"},{"alias_kind":"pith_short_12","alias_value":"7ZWU2YG7ZVAW","created_at":"2026-07-05T10:09:59.196216+00:00"},{"alias_kind":"pith_short_16","alias_value":"7ZWU2YG7ZVAWF5NJ","created_at":"2026-07-05T10:09:59.196216+00:00"},{"alias_kind":"pith_short_8","alias_value":"7ZWU2YG7","created_at":"2026-07-05T10:09:59.196216+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30474","citing_title":"Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX","json":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX.json","graph_json":"https://pith.science/api/pith-number/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/graph.json","events_json":"https://pith.science/api/pith-number/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/events.json","paper":"https://pith.science/paper/7ZWU2YG7"},"agent_actions":{"view_html":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX","download_json":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX.json","view_paper":"https://pith.science/paper/7ZWU2YG7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.03072&json=true","fetch_graph":"https://pith.science/api/pith-number/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/graph.json","fetch_events":"https://pith.science/api/pith-number/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/action/storage_attestation","attest_author":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/action/author_attestation","sign_citation":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/action/citation_signature","submit_replication":"https://pith.science/pith/7ZWU2YG7ZVAWF5NJI3DN6OQDSX/action/replication_record"}},"created_at":"2026-07-05T10:09:59.196216+00:00","updated_at":"2026-07-05T10:09:59.196216+00:00"}