{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2O75RD2RWM4WT3EDHTJJPOLNR7","short_pith_number":"pith:2O75RD2R","schema_version":"1.0","canonical_sha256":"d3bfd88f51b33969ec833cd297b96d8fdf6412aa273aeb088c1f2a45e306a9bb","source":{"kind":"arxiv","id":"2206.04585","version":2},"attestation_state":"computed","paper":{"title":"Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.RO","authors_text":"Luca Carlone, Rajat Talak, Siyi Hu, William Chen","submitted_at":"2022-06-09T16:05:35Z","abstract_excerpt":"Semantic 3D scene understanding is a problem of critical importance in robotics. While significant advances have been made in simultaneous localization and mapping algorithms, robots are still far from having the common sense knowledge about household objects and their locations of an average human. We introduce a novel method for leveraging common sense embedded within large language models for labelling rooms given the objects contained within. This algorithm has the added benefits of (i) requiring no task-specific pre-training (operating entirely in the zero-shot regime) and (ii) generalizi"},"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":"2206.04585","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-06-09T16:05:35Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"faa3b62ff7015678afab596a59f41d64918c1fb4fbdac93f746bac2414927dc1","abstract_canon_sha256":"301f20c1362506030657cac8f4913e2e0ba7ef9a84a4497fecdc313ce08dd19c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:33:00.819368Z","signature_b64":"KXkSvzi3F7hpCm0NYc/fiO0VjpVN2YIlGNY2wayTllHNAdfcwZEJjXXijkfjplkQT0jZ1Bxx7OaTy7e3rlJbDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3bfd88f51b33969ec833cd297b96d8fdf6412aa273aeb088c1f2a45e306a9bb","last_reissued_at":"2026-07-05T04:33:00.818737Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:33:00.818737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.RO","authors_text":"Luca Carlone, Rajat Talak, Siyi Hu, William Chen","submitted_at":"2022-06-09T16:05:35Z","abstract_excerpt":"Semantic 3D scene understanding is a problem of critical importance in robotics. While significant advances have been made in simultaneous localization and mapping algorithms, robots are still far from having the common sense knowledge about household objects and their locations of an average human. We introduce a novel method for leveraging common sense embedded within large language models for labelling rooms given the objects contained within. This algorithm has the added benefits of (i) requiring no task-specific pre-training (operating entirely in the zero-shot regime) and (ii) generalizi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.04585","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/2206.04585/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":"2206.04585","created_at":"2026-07-05T04:33:00.818822+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.04585v2","created_at":"2026-07-05T04:33:00.818822+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.04585","created_at":"2026-07-05T04:33:00.818822+00:00"},{"alias_kind":"pith_short_12","alias_value":"2O75RD2RWM4W","created_at":"2026-07-05T04:33:00.818822+00:00"},{"alias_kind":"pith_short_16","alias_value":"2O75RD2RWM4WT3ED","created_at":"2026-07-05T04:33:00.818822+00:00"},{"alias_kind":"pith_short_8","alias_value":"2O75RD2R","created_at":"2026-07-05T04:33:00.818822+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.06170","citing_title":"Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments","ref_index":65,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7","json":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7.json","graph_json":"https://pith.science/api/pith-number/2O75RD2RWM4WT3EDHTJJPOLNR7/graph.json","events_json":"https://pith.science/api/pith-number/2O75RD2RWM4WT3EDHTJJPOLNR7/events.json","paper":"https://pith.science/paper/2O75RD2R"},"agent_actions":{"view_html":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7","download_json":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7.json","view_paper":"https://pith.science/paper/2O75RD2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.04585&json=true","fetch_graph":"https://pith.science/api/pith-number/2O75RD2RWM4WT3EDHTJJPOLNR7/graph.json","fetch_events":"https://pith.science/api/pith-number/2O75RD2RWM4WT3EDHTJJPOLNR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7/action/storage_attestation","attest_author":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7/action/author_attestation","sign_citation":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7/action/citation_signature","submit_replication":"https://pith.science/pith/2O75RD2RWM4WT3EDHTJJPOLNR7/action/replication_record"}},"created_at":"2026-07-05T04:33:00.818822+00:00","updated_at":"2026-07-05T04:33:00.818822+00:00"}