{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LPE5NX6NLY5S2AFPEWZNSEZ53J","short_pith_number":"pith:LPE5NX6N","schema_version":"1.0","canonical_sha256":"5bc9d6dfcd5e3b2d00af25b2d9133dda76cc6c5e879af37f9648b5663c520dbd","source":{"kind":"arxiv","id":"2509.07646","version":1},"attestation_state":"computed","paper":{"title":"Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chuangchuang Zhou, Ming Chen, Pengxu Chang, Yanlong Peng, Yu Yan, Zhigang Wang, Ziwen He","submitted_at":"2025-09-09T12:15:04Z","abstract_excerpt":"In robots task and motion planning (TAMP), it is crucial to sample within the robot's configuration space to meet task-level global constraints and enhance the efficiency of subsequent motion planning. Due to the complexity of joint configuration sampling under multi-level constraints, traditional methods often lack efficiency. This paper introduces the principle of RobKiNet, a kinematics-informed neural network, for end-to-end sampling within the Continuous Feasible Set (CFS) under multiple constraints in configuration space, establishing its Optimization Expectation Model. Comparisons with t"},"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":"2509.07646","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-09-09T12:15:04Z","cross_cats_sorted":[],"title_canon_sha256":"a312051d75683a1e573320be4859f35074ba7f8586f0b20b26dbd2d732ee9573","abstract_canon_sha256":"74b2a67c6789156432091c4f63e73a19659dc8240864feff0217336df3178119"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:07:25.315470Z","signature_b64":"ZDBMyB9nUtp6yDOvl03VMzSK+gaTW4BvKu7GKizpH0o/KeJURLILL4Rc0E+fwf1MFCggBOi3l28LXacn3ImCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bc9d6dfcd5e3b2d00af25b2d9133dda76cc6c5e879af37f9648b5663c520dbd","last_reissued_at":"2026-07-05T12:07:25.314922Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:07:25.314922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chuangchuang Zhou, Ming Chen, Pengxu Chang, Yanlong Peng, Yu Yan, Zhigang Wang, Ziwen He","submitted_at":"2025-09-09T12:15:04Z","abstract_excerpt":"In robots task and motion planning (TAMP), it is crucial to sample within the robot's configuration space to meet task-level global constraints and enhance the efficiency of subsequent motion planning. Due to the complexity of joint configuration sampling under multi-level constraints, traditional methods often lack efficiency. This paper introduces the principle of RobKiNet, a kinematics-informed neural network, for end-to-end sampling within the Continuous Feasible Set (CFS) under multiple constraints in configuration space, establishing its Optimization Expectation Model. Comparisons with t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07646","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/2509.07646/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":"2509.07646","created_at":"2026-07-05T12:07:25.314989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.07646v1","created_at":"2026-07-05T12:07:25.314989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07646","created_at":"2026-07-05T12:07:25.314989+00:00"},{"alias_kind":"pith_short_12","alias_value":"LPE5NX6NLY5S","created_at":"2026-07-05T12:07:25.314989+00:00"},{"alias_kind":"pith_short_16","alias_value":"LPE5NX6NLY5S2AFP","created_at":"2026-07-05T12:07:25.314989+00:00"},{"alias_kind":"pith_short_8","alias_value":"LPE5NX6N","created_at":"2026-07-05T12:07:25.314989+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/LPE5NX6NLY5S2AFPEWZNSEZ53J","json":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J.json","graph_json":"https://pith.science/api/pith-number/LPE5NX6NLY5S2AFPEWZNSEZ53J/graph.json","events_json":"https://pith.science/api/pith-number/LPE5NX6NLY5S2AFPEWZNSEZ53J/events.json","paper":"https://pith.science/paper/LPE5NX6N"},"agent_actions":{"view_html":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J","download_json":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J.json","view_paper":"https://pith.science/paper/LPE5NX6N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.07646&json=true","fetch_graph":"https://pith.science/api/pith-number/LPE5NX6NLY5S2AFPEWZNSEZ53J/graph.json","fetch_events":"https://pith.science/api/pith-number/LPE5NX6NLY5S2AFPEWZNSEZ53J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J/action/storage_attestation","attest_author":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J/action/author_attestation","sign_citation":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J/action/citation_signature","submit_replication":"https://pith.science/pith/LPE5NX6NLY5S2AFPEWZNSEZ53J/action/replication_record"}},"created_at":"2026-07-05T12:07:25.314989+00:00","updated_at":"2026-07-05T12:07:25.314989+00:00"}