{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VKAY6M5UFHMQZWU644LD75IHM6","short_pith_number":"pith:VKAY6M5U","schema_version":"1.0","canonical_sha256":"aa818f33b429d90cda9ee7163ff50767b0c5cf1cfaec44a1a266989121df0686","source":{"kind":"arxiv","id":"2409.16879","version":2},"attestation_state":"computed","paper":{"title":"GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fethiye Irmak Dogan, Gizem Cinar, Hatice Gunes, Umut Ozyurt","submitted_at":"2024-09-25T12:44:13Z","abstract_excerpt":"When operating in human environments, robots need to handle complex tasks while both adhering to social norms and accommodating individual preferences. For instance, based on common sense knowledge, a household robot can predict that it should avoid vacuuming during a social gathering, but it may still be uncertain whether it should vacuum before or after having guests. In such cases, integrating common-sense knowledge with human preferences, often conveyed through human explanations, is fundamental yet a challenge for existing systems. In this paper, we introduce GRACE, a novel approach addre"},"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":"2409.16879","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-09-25T12:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"60727712bfb1ce80320989ca3b8ffc84e79af45bee2d0ff445e1c908584e3b1a","abstract_canon_sha256":"c8cecff87de7b304ef085b4669fea99f3a265687dcc6252ebb99d02c65148017"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:39.460797Z","signature_b64":"a5B3Lbar/jl+y7Uvu145K8EEk5veBgHQT85ydRyil5OIpBDpCsNpGkvETF0wW+/K7qg8gMp6B/epVg6bJ3//Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa818f33b429d90cda9ee7163ff50767b0c5cf1cfaec44a1a266989121df0686","last_reissued_at":"2026-07-05T10:43:39.460261Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:39.460261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fethiye Irmak Dogan, Gizem Cinar, Hatice Gunes, Umut Ozyurt","submitted_at":"2024-09-25T12:44:13Z","abstract_excerpt":"When operating in human environments, robots need to handle complex tasks while both adhering to social norms and accommodating individual preferences. For instance, based on common sense knowledge, a household robot can predict that it should avoid vacuuming during a social gathering, but it may still be uncertain whether it should vacuum before or after having guests. In such cases, integrating common-sense knowledge with human preferences, often conveyed through human explanations, is fundamental yet a challenge for existing systems. In this paper, we introduce GRACE, a novel approach addre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.16879","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/2409.16879/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":"2409.16879","created_at":"2026-07-05T10:43:39.460328+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.16879v2","created_at":"2026-07-05T10:43:39.460328+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.16879","created_at":"2026-07-05T10:43:39.460328+00:00"},{"alias_kind":"pith_short_12","alias_value":"VKAY6M5UFHMQ","created_at":"2026-07-05T10:43:39.460328+00:00"},{"alias_kind":"pith_short_16","alias_value":"VKAY6M5UFHMQZWU6","created_at":"2026-07-05T10:43:39.460328+00:00"},{"alias_kind":"pith_short_8","alias_value":"VKAY6M5U","created_at":"2026-07-05T10:43:39.460328+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.18711","citing_title":"LLM-enhanced Interactions in Human-Robot Collaborative Drawing with Older Adults","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6","json":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6.json","graph_json":"https://pith.science/api/pith-number/VKAY6M5UFHMQZWU644LD75IHM6/graph.json","events_json":"https://pith.science/api/pith-number/VKAY6M5UFHMQZWU644LD75IHM6/events.json","paper":"https://pith.science/paper/VKAY6M5U"},"agent_actions":{"view_html":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6","download_json":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6.json","view_paper":"https://pith.science/paper/VKAY6M5U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.16879&json=true","fetch_graph":"https://pith.science/api/pith-number/VKAY6M5UFHMQZWU644LD75IHM6/graph.json","fetch_events":"https://pith.science/api/pith-number/VKAY6M5UFHMQZWU644LD75IHM6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6/action/storage_attestation","attest_author":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6/action/author_attestation","sign_citation":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6/action/citation_signature","submit_replication":"https://pith.science/pith/VKAY6M5UFHMQZWU644LD75IHM6/action/replication_record"}},"created_at":"2026-07-05T10:43:39.460328+00:00","updated_at":"2026-07-05T10:43:39.460328+00:00"}