{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BCL4WVN2LX2PDBCS7IJ7ZVKQBR","short_pith_number":"pith:BCL4WVN2","schema_version":"1.0","canonical_sha256":"0897cb55ba5df4f18452fa13fcd5500c7b6e828acad9010e94c49ffa83b5db58","source":{"kind":"arxiv","id":"2312.12036","version":3},"attestation_state":"computed","paper":{"title":"LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Federico Ceola, Krishan Rana, Lorenzo Natale, Niko S\\\"underhauf","submitted_at":"2023-12-19T10:45:56Z","abstract_excerpt":"Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics. While recent progress in language-conditioned imitation learning and offline reinforcement learning has demonstrated impressive performance across a wide range of tasks, they are typically limited to short-horizon tasks -- not reflective of those a home robot would be expected to complete. While existing architectures have the potential to learn these desired behaviours, the lack of the necessary long-horizon, multi-step datasets for real robotic systems poses a significant challe"},"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":"2312.12036","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-12-19T10:45:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"70a721f327291864ef13d0ced4cf7f882cbc5e87db08db0cc3721af590d9aa8b","abstract_canon_sha256":"e6f0a3262b46dfe3b5ac1bc68c0072ecd7471b35aad023b7b432f9392b7ee511"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:55.909734Z","signature_b64":"1iOwZXojgC69/BPdvui3MyCYYnjWZCEHu3lX/Syd8id+8JPNKSYlDbImq4nZ2df+Muwj1PKPRPFAv/s20L9QBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0897cb55ba5df4f18452fa13fcd5500c7b6e828acad9010e94c49ffa83b5db58","last_reissued_at":"2026-07-05T08:38:55.909244Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:55.909244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Federico Ceola, Krishan Rana, Lorenzo Natale, Niko S\\\"underhauf","submitted_at":"2023-12-19T10:45:56Z","abstract_excerpt":"Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics. While recent progress in language-conditioned imitation learning and offline reinforcement learning has demonstrated impressive performance across a wide range of tasks, they are typically limited to short-horizon tasks -- not reflective of those a home robot would be expected to complete. While existing architectures have the potential to learn these desired behaviours, the lack of the necessary long-horizon, multi-step datasets for real robotic systems poses a significant challe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.12036","kind":"arxiv","version":3},"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/2312.12036/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":"2312.12036","created_at":"2026-07-05T08:38:55.909306+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.12036v3","created_at":"2026-07-05T08:38:55.909306+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.12036","created_at":"2026-07-05T08:38:55.909306+00:00"},{"alias_kind":"pith_short_12","alias_value":"BCL4WVN2LX2P","created_at":"2026-07-05T08:38:55.909306+00:00"},{"alias_kind":"pith_short_16","alias_value":"BCL4WVN2LX2PDBCS","created_at":"2026-07-05T08:38:55.909306+00:00"},{"alias_kind":"pith_short_8","alias_value":"BCL4WVN2","created_at":"2026-07-05T08:38:55.909306+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2503.10631","citing_title":"HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model","ref_index":115,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12565","citing_title":"Scalable Trajectory Generation for Whole-Body Mobile Manipulation","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR","json":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR.json","graph_json":"https://pith.science/api/pith-number/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/graph.json","events_json":"https://pith.science/api/pith-number/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/events.json","paper":"https://pith.science/paper/BCL4WVN2"},"agent_actions":{"view_html":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR","download_json":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR.json","view_paper":"https://pith.science/paper/BCL4WVN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.12036&json=true","fetch_graph":"https://pith.science/api/pith-number/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/graph.json","fetch_events":"https://pith.science/api/pith-number/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/action/storage_attestation","attest_author":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/action/author_attestation","sign_citation":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/action/citation_signature","submit_replication":"https://pith.science/pith/BCL4WVN2LX2PDBCS7IJ7ZVKQBR/action/replication_record"}},"created_at":"2026-07-05T08:38:55.909306+00:00","updated_at":"2026-07-05T08:38:55.909306+00:00"}