{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VO2UWCZVLGSUUD3AEDSCYBWS7U","short_pith_number":"pith:VO2UWCZV","schema_version":"1.0","canonical_sha256":"abb54b0b3559a54a0f6020e42c06d2fd39ed83e976fb2d8e663eb8b31f0f986c","source":{"kind":"arxiv","id":"1902.10841","version":2},"attestation_state":"computed","paper":{"title":"Efficient Grasp Planning and Execution with Multi-Fingered Hands by Surface Fitting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Masayoshi Tomizuka, Yongxiang Fan","submitted_at":"2019-02-28T00:05:31Z","abstract_excerpt":"This paper introduces a framework to plan grasps with multi-fingered hands. The framework includes a multi-dimensional iterative surface fitting (MDISF) for grasp planning and a grasp trajectory optimization (GTO) for grasp imagination. The MDISF algorithm searches for optimal contact regions and hand configurations by minimizing the collision and surface fitting error, and the GTO algorithm generates optimal finger trajectories to reach the highly ranked grasp configurations and avoid collision with the environment. The proposed grasp planning and imagination framework considers the collision"},"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":"1902.10841","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-02-28T00:05:31Z","cross_cats_sorted":[],"title_canon_sha256":"aefc86dbb58886cf0247db38b3ef6b585cd235d4ad129627e08e050b1bfcdfdc","abstract_canon_sha256":"5ccc0b15844c6ce5559bf8264c7477815e2e2928270d9a510904bc310be92ca1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:17.454511Z","signature_b64":"F7P4NfvS01Jh2A0P8pgK9CBRWgDlnykpIickvQg/q91J8HvHKyZmEaJSjtQkDVvbyuvwatk+YOIz7nTzGfFpDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abb54b0b3559a54a0f6020e42c06d2fd39ed83e976fb2d8e663eb8b31f0f986c","last_reissued_at":"2026-05-17T23:40:17.453885Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:17.453885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Grasp Planning and Execution with Multi-Fingered Hands by Surface Fitting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Masayoshi Tomizuka, Yongxiang Fan","submitted_at":"2019-02-28T00:05:31Z","abstract_excerpt":"This paper introduces a framework to plan grasps with multi-fingered hands. The framework includes a multi-dimensional iterative surface fitting (MDISF) for grasp planning and a grasp trajectory optimization (GTO) for grasp imagination. The MDISF algorithm searches for optimal contact regions and hand configurations by minimizing the collision and surface fitting error, and the GTO algorithm generates optimal finger trajectories to reach the highly ranked grasp configurations and avoid collision with the environment. The proposed grasp planning and imagination framework considers the collision"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10841","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":""},"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":"1902.10841","created_at":"2026-05-17T23:40:17.453994+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.10841v2","created_at":"2026-05-17T23:40:17.453994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10841","created_at":"2026-05-17T23:40:17.453994+00:00"},{"alias_kind":"pith_short_12","alias_value":"VO2UWCZVLGSU","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"VO2UWCZVLGSUUD3A","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"VO2UWCZV","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U","json":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U.json","graph_json":"https://pith.science/api/pith-number/VO2UWCZVLGSUUD3AEDSCYBWS7U/graph.json","events_json":"https://pith.science/api/pith-number/VO2UWCZVLGSUUD3AEDSCYBWS7U/events.json","paper":"https://pith.science/paper/VO2UWCZV"},"agent_actions":{"view_html":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U","download_json":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U.json","view_paper":"https://pith.science/paper/VO2UWCZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.10841&json=true","fetch_graph":"https://pith.science/api/pith-number/VO2UWCZVLGSUUD3AEDSCYBWS7U/graph.json","fetch_events":"https://pith.science/api/pith-number/VO2UWCZVLGSUUD3AEDSCYBWS7U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U/action/storage_attestation","attest_author":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U/action/author_attestation","sign_citation":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U/action/citation_signature","submit_replication":"https://pith.science/pith/VO2UWCZVLGSUUD3AEDSCYBWS7U/action/replication_record"}},"created_at":"2026-05-17T23:40:17.453994+00:00","updated_at":"2026-05-17T23:40:17.453994+00:00"}