{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VMPSEKSPOL3PVL6ZVSI6JPOEWI","short_pith_number":"pith:VMPSEKSP","schema_version":"1.0","canonical_sha256":"ab1f222a4f72f6faafd9ac91e4bdc4b234e4d7db112afb825ee1b3d571ae5a34","source":{"kind":"arxiv","id":"2503.17309","version":1},"attestation_state":"computed","paper":{"title":"LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Cornelius Weber, Kun Chu, Stefan Wermter, Xufeng Zhao","submitted_at":"2025-03-21T17:04:01Z","abstract_excerpt":"Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands. Existing works predominantly focus on attaining human-level manipulation skills for robotic hands, yet little attention has been paid to task planning on long-horizon timescales. With their outstanding in-context learning and zero-shot generation abilities, Large Language Models (LLMs) have been applied and grounded in diverse robotic embodiments to facilitate task planning. However, LLMs still suffer "},"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":"2503.17309","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-21T17:04:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"63cd1c3e4b965b2f28b2c9e5d124b712d03938e267af7d4b73f70fc2326b141e","abstract_canon_sha256":"be1a19683251bf319b5da55eae8f5b95319c1f2f0100abc53279d47711615afb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:03.297081Z","signature_b64":"8xokmoYme3+uff/k/TRHE4x5TgdB97lTkjOkqSeMZ10g1EhlYgTBJguJu02N8WeyLYc4kAqBnThvo0NBZoFoCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab1f222a4f72f6faafd9ac91e4bdc4b234e4d7db112afb825ee1b3d571ae5a34","last_reissued_at":"2026-07-05T10:37:03.296281Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:03.296281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Cornelius Weber, Kun Chu, Stefan Wermter, Xufeng Zhao","submitted_at":"2025-03-21T17:04:01Z","abstract_excerpt":"Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands. Existing works predominantly focus on attaining human-level manipulation skills for robotic hands, yet little attention has been paid to task planning on long-horizon timescales. With their outstanding in-context learning and zero-shot generation abilities, Large Language Models (LLMs) have been applied and grounded in diverse robotic embodiments to facilitate task planning. However, LLMs still suffer "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17309","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/2503.17309/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":"2503.17309","created_at":"2026-07-05T10:37:03.296385+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17309v1","created_at":"2026-07-05T10:37:03.296385+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17309","created_at":"2026-07-05T10:37:03.296385+00:00"},{"alias_kind":"pith_short_12","alias_value":"VMPSEKSPOL3P","created_at":"2026-07-05T10:37:03.296385+00:00"},{"alias_kind":"pith_short_16","alias_value":"VMPSEKSPOL3PVL6Z","created_at":"2026-07-05T10:37:03.296385+00:00"},{"alias_kind":"pith_short_8","alias_value":"VMPSEKSP","created_at":"2026-07-05T10:37:03.296385+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19897","citing_title":"One-to-Two Acting: A Novel Framework for Single-arm Agent Action Expansion to Dual Arms","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2602.03433","citing_title":"When control meets large language models: From words to dynamics","ref_index":180,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09580","citing_title":"OOWM: Structuring Embodied Reasoning and Planning via Object-Oriented Programmatic World Modeling","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2511.20857","citing_title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","ref_index":271,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI","json":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI.json","graph_json":"https://pith.science/api/pith-number/VMPSEKSPOL3PVL6ZVSI6JPOEWI/graph.json","events_json":"https://pith.science/api/pith-number/VMPSEKSPOL3PVL6ZVSI6JPOEWI/events.json","paper":"https://pith.science/paper/VMPSEKSP"},"agent_actions":{"view_html":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI","download_json":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI.json","view_paper":"https://pith.science/paper/VMPSEKSP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17309&json=true","fetch_graph":"https://pith.science/api/pith-number/VMPSEKSPOL3PVL6ZVSI6JPOEWI/graph.json","fetch_events":"https://pith.science/api/pith-number/VMPSEKSPOL3PVL6ZVSI6JPOEWI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI/action/storage_attestation","attest_author":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI/action/author_attestation","sign_citation":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI/action/citation_signature","submit_replication":"https://pith.science/pith/VMPSEKSPOL3PVL6ZVSI6JPOEWI/action/replication_record"}},"created_at":"2026-07-05T10:37:03.296385+00:00","updated_at":"2026-07-05T10:37:03.296385+00:00"}