{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HO6KQFEU2AQQ47MS7QHE7OSPF2","short_pith_number":"pith:HO6KQFEU","schema_version":"1.0","canonical_sha256":"3bbca81494d0210e7d92fc0e4fba4f2e8dafa46e23ad3775afc8dd332d0a4b03","source":{"kind":"arxiv","id":"1907.00388","version":1},"attestation_state":"computed","paper":{"title":"Reinforcement Learning for Robotic Time-optimal Path Tracking Using Prior Knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Jiadong Xiao, Lin Li, Tie Zhang, Yanbiao Zou","submitted_at":"2019-06-30T14:48:10Z","abstract_excerpt":"Time-optimal path tracking, as a significant tool for industrial robots, has attracted the attention of numerous researchers. In most time-optimal path tracking problems, the actuator torque constraints are assumed to be conservative, which ignores the motor characteristic; i.e., the actuator torque constraints are velocity-dependent, and the relationship between torque and velocity is piecewise linear. However, considering that the motor characteristics increase the solving difficulty, in this study, an improved Q-learning algorithm for robotic time-optimal path tracking using prior knowledge"},"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":"1907.00388","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-06-30T14:48:10Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"5917c854deb4eaf9d9979c52209c6196e4e3db6d4b30fd91b9e7b5fb79b929fe","abstract_canon_sha256":"25f897793080575de56182cc6a549c6ce11ab0f4b75ee5aa1f96f4d51821b6f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:00.559134Z","signature_b64":"Z6Jl/2vGaNp+irhXGujmMVyZoNfwrZ77gfCJKT/aim8Cqh++qG9KB/WSa0hQgzH2Ojm8bIPVNo8EHdDrvbbMCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3bbca81494d0210e7d92fc0e4fba4f2e8dafa46e23ad3775afc8dd332d0a4b03","last_reissued_at":"2026-05-17T23:41:00.558367Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:00.558367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reinforcement Learning for Robotic Time-optimal Path Tracking Using Prior Knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Jiadong Xiao, Lin Li, Tie Zhang, Yanbiao Zou","submitted_at":"2019-06-30T14:48:10Z","abstract_excerpt":"Time-optimal path tracking, as a significant tool for industrial robots, has attracted the attention of numerous researchers. In most time-optimal path tracking problems, the actuator torque constraints are assumed to be conservative, which ignores the motor characteristic; i.e., the actuator torque constraints are velocity-dependent, and the relationship between torque and velocity is piecewise linear. However, considering that the motor characteristics increase the solving difficulty, in this study, an improved Q-learning algorithm for robotic time-optimal path tracking using prior knowledge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.00388","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":""},"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":"1907.00388","created_at":"2026-05-17T23:41:00.558477+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.00388v1","created_at":"2026-05-17T23:41:00.558477+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.00388","created_at":"2026-05-17T23:41:00.558477+00:00"},{"alias_kind":"pith_short_12","alias_value":"HO6KQFEU2AQQ","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"HO6KQFEU2AQQ47MS","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"HO6KQFEU","created_at":"2026-05-18T12:33:18.533446+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/HO6KQFEU2AQQ47MS7QHE7OSPF2","json":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2.json","graph_json":"https://pith.science/api/pith-number/HO6KQFEU2AQQ47MS7QHE7OSPF2/graph.json","events_json":"https://pith.science/api/pith-number/HO6KQFEU2AQQ47MS7QHE7OSPF2/events.json","paper":"https://pith.science/paper/HO6KQFEU"},"agent_actions":{"view_html":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2","download_json":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2.json","view_paper":"https://pith.science/paper/HO6KQFEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.00388&json=true","fetch_graph":"https://pith.science/api/pith-number/HO6KQFEU2AQQ47MS7QHE7OSPF2/graph.json","fetch_events":"https://pith.science/api/pith-number/HO6KQFEU2AQQ47MS7QHE7OSPF2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2/action/storage_attestation","attest_author":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2/action/author_attestation","sign_citation":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2/action/citation_signature","submit_replication":"https://pith.science/pith/HO6KQFEU2AQQ47MS7QHE7OSPF2/action/replication_record"}},"created_at":"2026-05-17T23:41:00.558477+00:00","updated_at":"2026-05-17T23:41:00.558477+00:00"}