{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:72HIAFEARWKD6RZ3A3REFYRCZE","short_pith_number":"pith:72HIAFEA","schema_version":"1.0","canonical_sha256":"fe8e8014808d943f473b06e242e222c9243293a670988d1782fdb77f88e42828","source":{"kind":"arxiv","id":"2409.13837","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Robot Perception in Construction Environments using 4D BIM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Mani Amani, Reza Akhavian","submitted_at":"2024-09-20T18:22:25Z","abstract_excerpt":"Human Activity Recognition (HAR) is a pivotal component of robot perception for physical Human Robot Interaction (pHRI) tasks. In construction robotics, it is vital that robots have an accurate and robust perception of worker activities. This enhanced perception is the foundation of trustworthy and safe Human-Robot Collaboration (HRC) in an industrial setting. Many developed HAR algorithms lack the robustness and adaptability to ensure seamless HRC. Recent works have employed multi-modal approaches to increase feature considerations. This paper further expands previous research to include 4D b"},"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":"2409.13837","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-09-20T18:22:25Z","cross_cats_sorted":[],"title_canon_sha256":"72358f0e49cdcea153156150995817b0b75d99add4a1525882517613144d8920","abstract_canon_sha256":"f558f77404b3b7f3660e7d9b35745a81d9ed6104cef6415fa21cba0a4b7db27f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:07.393548Z","signature_b64":"y43gKA95tMGhqhn3c+A75XlFMWG1L540kMRsixj9it3IxtPmDZl/ks5w+GSuJRiW+Jf3ya5WH2gb8ZsOPOAeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe8e8014808d943f473b06e242e222c9243293a670988d1782fdb77f88e42828","last_reissued_at":"2026-07-05T09:10:07.393065Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:07.393065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Robot Perception in Construction Environments using 4D BIM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Mani Amani, Reza Akhavian","submitted_at":"2024-09-20T18:22:25Z","abstract_excerpt":"Human Activity Recognition (HAR) is a pivotal component of robot perception for physical Human Robot Interaction (pHRI) tasks. In construction robotics, it is vital that robots have an accurate and robust perception of worker activities. This enhanced perception is the foundation of trustworthy and safe Human-Robot Collaboration (HRC) in an industrial setting. Many developed HAR algorithms lack the robustness and adaptability to ensure seamless HRC. Recent works have employed multi-modal approaches to increase feature considerations. This paper further expands previous research to include 4D b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13837","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/2409.13837/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":"2409.13837","created_at":"2026-07-05T09:10:07.393124+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.13837v1","created_at":"2026-07-05T09:10:07.393124+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13837","created_at":"2026-07-05T09:10:07.393124+00:00"},{"alias_kind":"pith_short_12","alias_value":"72HIAFEARWKD","created_at":"2026-07-05T09:10:07.393124+00:00"},{"alias_kind":"pith_short_16","alias_value":"72HIAFEARWKD6RZ3","created_at":"2026-07-05T09:10:07.393124+00:00"},{"alias_kind":"pith_short_8","alias_value":"72HIAFEA","created_at":"2026-07-05T09:10:07.393124+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.02876","citing_title":"Generalizable Skill Learning for Construction Robots with Crowdsourced Natural Language Instructions, Composable Skills Standardization, and Large Language Model","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE","json":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE.json","graph_json":"https://pith.science/api/pith-number/72HIAFEARWKD6RZ3A3REFYRCZE/graph.json","events_json":"https://pith.science/api/pith-number/72HIAFEARWKD6RZ3A3REFYRCZE/events.json","paper":"https://pith.science/paper/72HIAFEA"},"agent_actions":{"view_html":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE","download_json":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE.json","view_paper":"https://pith.science/paper/72HIAFEA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.13837&json=true","fetch_graph":"https://pith.science/api/pith-number/72HIAFEARWKD6RZ3A3REFYRCZE/graph.json","fetch_events":"https://pith.science/api/pith-number/72HIAFEARWKD6RZ3A3REFYRCZE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE/action/storage_attestation","attest_author":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE/action/author_attestation","sign_citation":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE/action/citation_signature","submit_replication":"https://pith.science/pith/72HIAFEARWKD6RZ3A3REFYRCZE/action/replication_record"}},"created_at":"2026-07-05T09:10:07.393124+00:00","updated_at":"2026-07-05T09:10:07.393124+00:00"}