{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CCTDQDNYC767C4OMY7ZCRO2ROL","short_pith_number":"pith:CCTDQDNY","schema_version":"1.0","canonical_sha256":"10a6380db817fdf171ccc7f228bb5172d79e9d5db7b1daee4f0b281fd3b2e0b4","source":{"kind":"arxiv","id":"2203.10568","version":1},"attestation_state":"computed","paper":{"title":"Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Georgia Chalvatzaki, Jan Peters, Lei Xu, Tianyu Ren","submitted_at":"2022-03-20T14:41:32Z","abstract_excerpt":"As robots play an increasingly important role in the industrial, the expectations about their applications for everyday living tasks are getting higher. Robots need to perform long-horizon tasks that consist of several sub-tasks that need to be accomplished. Task and Motion Planning (TAMP) provides a hierarchical framework to handle the sequential nature of manipulation tasks by interleaving a symbolic task planner that generates a possible action sequence, with a motion planner that checks the kinematic feasibility in the geometric world, generating robot trajectories if several constraints a"},"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":"2203.10568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-03-20T14:41:32Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"3dfb425747456dc81f0066ca2b733679655b03d10b1aca9f4923c8d00321fa1d","abstract_canon_sha256":"c84587b767765a73d793508cf7b9e2816f1bcea260197e49e46bdf73891fcf52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:54.432036Z","signature_b64":"iM1/rFbWh3NeipDL13yVj86kCA1nxFxEOTt/0XVE6cigJdx5qxih9MgCt6FqRVo3w3nkSxvTnpxk6Wc+gPdEAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10a6380db817fdf171ccc7f228bb5172d79e9d5db7b1daee4f0b281fd3b2e0b4","last_reissued_at":"2026-07-05T04:06:54.431632Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:54.431632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Georgia Chalvatzaki, Jan Peters, Lei Xu, Tianyu Ren","submitted_at":"2022-03-20T14:41:32Z","abstract_excerpt":"As robots play an increasingly important role in the industrial, the expectations about their applications for everyday living tasks are getting higher. Robots need to perform long-horizon tasks that consist of several sub-tasks that need to be accomplished. Task and Motion Planning (TAMP) provides a hierarchical framework to handle the sequential nature of manipulation tasks by interleaving a symbolic task planner that generates a possible action sequence, with a motion planner that checks the kinematic feasibility in the geometric world, generating robot trajectories if several constraints a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10568","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/2203.10568/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":"2203.10568","created_at":"2026-07-05T04:06:54.431687+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.10568v1","created_at":"2026-07-05T04:06:54.431687+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10568","created_at":"2026-07-05T04:06:54.431687+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCTDQDNYC767","created_at":"2026-07-05T04:06:54.431687+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCTDQDNYC767C4OM","created_at":"2026-07-05T04:06:54.431687+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCTDQDNY","created_at":"2026-07-05T04:06:54.431687+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26700","citing_title":"Learning Motion Feasibility from Point Clouds in Cluttered Environments","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15975","citing_title":"Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning","ref_index":142,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL","json":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL.json","graph_json":"https://pith.science/api/pith-number/CCTDQDNYC767C4OMY7ZCRO2ROL/graph.json","events_json":"https://pith.science/api/pith-number/CCTDQDNYC767C4OMY7ZCRO2ROL/events.json","paper":"https://pith.science/paper/CCTDQDNY"},"agent_actions":{"view_html":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL","download_json":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL.json","view_paper":"https://pith.science/paper/CCTDQDNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.10568&json=true","fetch_graph":"https://pith.science/api/pith-number/CCTDQDNYC767C4OMY7ZCRO2ROL/graph.json","fetch_events":"https://pith.science/api/pith-number/CCTDQDNYC767C4OMY7ZCRO2ROL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL/action/storage_attestation","attest_author":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL/action/author_attestation","sign_citation":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL/action/citation_signature","submit_replication":"https://pith.science/pith/CCTDQDNYC767C4OMY7ZCRO2ROL/action/replication_record"}},"created_at":"2026-07-05T04:06:54.431687+00:00","updated_at":"2026-07-05T04:06:54.431687+00:00"}