{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NLCJWUZUEJVO725BIKCFK7NIP5","short_pith_number":"pith:NLCJWUZU","schema_version":"1.0","canonical_sha256":"6ac49b5334226aefeba14284557da87f6f41ad47e739ca88d0ffebc51369d062","source":{"kind":"arxiv","id":"2501.18516","version":2},"attestation_state":"computed","paper":{"title":"Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Erich Graf, Guanqun Cao, John Oyekan, Ryan McKenna","submitted_at":"2025-01-30T17:28:11Z","abstract_excerpt":"Object manipulation for rearrangement into a specific goal state is a significant task for collaborative robots. Accurately determining object placement is a key challenge, as misalignment can increase task complexity and the risk of collisions, affecting the efficiency of the rearrangement process. Most current methods heavily rely on pre-collected datasets to train the model for predicting the goal position. As a result, these methods are restricted to specific instructions, which limits their broader applicability and generalisation. In this paper, we propose a framework of flexible languag"},"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":"2501.18516","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-01-30T17:28:11Z","cross_cats_sorted":[],"title_canon_sha256":"fc4b74f5e3006f1970b2d061677e3ff62f4b8bffe335aed98de8d7d1fac6af61","abstract_canon_sha256":"17ce68534cc56d6d50f2fb61e1a9adda5aa0d3ed2858b0c31b4f6e771813cb6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:30.473079Z","signature_b64":"0uUaITuBQGDFC+E+JK+6TA+xXHS68lS3BUV7FI3urmfIp00CzYbXuzVZ69GYn/i+N0cT27OHIvXd1IOogj23Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ac49b5334226aefeba14284557da87f6f41ad47e739ca88d0ffebc51369d062","last_reissued_at":"2026-07-05T10:24:30.472309Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:30.472309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Erich Graf, Guanqun Cao, John Oyekan, Ryan McKenna","submitted_at":"2025-01-30T17:28:11Z","abstract_excerpt":"Object manipulation for rearrangement into a specific goal state is a significant task for collaborative robots. Accurately determining object placement is a key challenge, as misalignment can increase task complexity and the risk of collisions, affecting the efficiency of the rearrangement process. Most current methods heavily rely on pre-collected datasets to train the model for predicting the goal position. As a result, these methods are restricted to specific instructions, which limits their broader applicability and generalisation. In this paper, we propose a framework of flexible languag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18516","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.18516/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":"2501.18516","created_at":"2026-07-05T10:24:30.472434+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.18516v2","created_at":"2026-07-05T10:24:30.472434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18516","created_at":"2026-07-05T10:24:30.472434+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLCJWUZUEJVO","created_at":"2026-07-05T10:24:30.472434+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLCJWUZUEJVO725B","created_at":"2026-07-05T10:24:30.472434+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLCJWUZU","created_at":"2026-07-05T10:24:30.472434+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/NLCJWUZUEJVO725BIKCFK7NIP5","json":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5.json","graph_json":"https://pith.science/api/pith-number/NLCJWUZUEJVO725BIKCFK7NIP5/graph.json","events_json":"https://pith.science/api/pith-number/NLCJWUZUEJVO725BIKCFK7NIP5/events.json","paper":"https://pith.science/paper/NLCJWUZU"},"agent_actions":{"view_html":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5","download_json":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5.json","view_paper":"https://pith.science/paper/NLCJWUZU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.18516&json=true","fetch_graph":"https://pith.science/api/pith-number/NLCJWUZUEJVO725BIKCFK7NIP5/graph.json","fetch_events":"https://pith.science/api/pith-number/NLCJWUZUEJVO725BIKCFK7NIP5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5/action/storage_attestation","attest_author":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5/action/author_attestation","sign_citation":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5/action/citation_signature","submit_replication":"https://pith.science/pith/NLCJWUZUEJVO725BIKCFK7NIP5/action/replication_record"}},"created_at":"2026-07-05T10:24:30.472434+00:00","updated_at":"2026-07-05T10:24:30.472434+00:00"}