{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2XLB5ATXX2HPDST4LG4DSVWPY5","short_pith_number":"pith:2XLB5ATX","schema_version":"1.0","canonical_sha256":"d5d61e8277be8ef1ca7c59b83956cfc774fa839f4486815c00b8b878441baf45","source":{"kind":"arxiv","id":"2304.11018","version":1},"attestation_state":"computed","paper":{"title":"Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.RO","authors_text":"Hengxu You, Jing Du, Qi Zhu, Tianyu Zhou, Yang Ye","submitted_at":"2023-04-21T15:04:41Z","abstract_excerpt":"Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the ability of the current"},"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":"2304.11018","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-04-21T15:04:41Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"7d7598b9e78b94fa2fb93a25fd4eb7ec0a465ebfee131c215b60eadad24c1b32","abstract_canon_sha256":"b59e9a96fd266648649a3539e610c354709ae3c2ad0f8242b347ac683e0865fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:03:17.998858Z","signature_b64":"XxiPueQekDzEAs3HaVpUS1+ED0/JA85QqJF5/lLyl9CU9FTaf8cYQrrfyPZVAbjlT06pvqKYu+Rlxt7eS2z2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5d61e8277be8ef1ca7c59b83956cfc774fa839f4486815c00b8b878441baf45","last_reissued_at":"2026-07-05T06:03:17.998400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:03:17.998400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.RO","authors_text":"Hengxu You, Jing Du, Qi Zhu, Tianyu Zhou, Yang Ye","submitted_at":"2023-04-21T15:04:41Z","abstract_excerpt":"Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the ability of the current"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.11018","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/2304.11018/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":"2304.11018","created_at":"2026-07-05T06:03:17.998462+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.11018v1","created_at":"2026-07-05T06:03:17.998462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.11018","created_at":"2026-07-05T06:03:17.998462+00:00"},{"alias_kind":"pith_short_12","alias_value":"2XLB5ATXX2HP","created_at":"2026-07-05T06:03:17.998462+00:00"},{"alias_kind":"pith_short_16","alias_value":"2XLB5ATXX2HPDST4","created_at":"2026-07-05T06:03:17.998462+00:00"},{"alias_kind":"pith_short_8","alias_value":"2XLB5ATX","created_at":"2026-07-05T06:03:17.998462+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18872","citing_title":"EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5","json":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5.json","graph_json":"https://pith.science/api/pith-number/2XLB5ATXX2HPDST4LG4DSVWPY5/graph.json","events_json":"https://pith.science/api/pith-number/2XLB5ATXX2HPDST4LG4DSVWPY5/events.json","paper":"https://pith.science/paper/2XLB5ATX"},"agent_actions":{"view_html":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5","download_json":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5.json","view_paper":"https://pith.science/paper/2XLB5ATX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.11018&json=true","fetch_graph":"https://pith.science/api/pith-number/2XLB5ATXX2HPDST4LG4DSVWPY5/graph.json","fetch_events":"https://pith.science/api/pith-number/2XLB5ATXX2HPDST4LG4DSVWPY5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5/action/storage_attestation","attest_author":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5/action/author_attestation","sign_citation":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5/action/citation_signature","submit_replication":"https://pith.science/pith/2XLB5ATXX2HPDST4LG4DSVWPY5/action/replication_record"}},"created_at":"2026-07-05T06:03:17.998462+00:00","updated_at":"2026-07-05T06:03:17.998462+00:00"}