{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EVURUNXFDLXKT66NCBRYETH4DX","short_pith_number":"pith:EVURUNXF","schema_version":"1.0","canonical_sha256":"25691a36e51aeea9fbcd1063824cfc1df348008dd26f8904f00b147410ef23d8","source":{"kind":"arxiv","id":"2508.15354","version":1},"attestation_state":"computed","paper":{"title":"Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Changhao Chen, Chaoran Xiong, Fangwen Yu, Ling Pei, Songpengchen Xia, Yue Wang, Yulong Huang","submitted_at":"2025-08-21T08:33:51Z","abstract_excerpt":"Embodied navigation (EN) advances traditional navigation by enabling robots to perform complex egocentric tasks through sensing, social, and motion intelligence. In contrast to classic methodologies that rely on explicit localization and pre-defined maps, EN leverages egocentric perception and human-like interaction strategies. This survey introduces a comprehensive EN formulation structured into five stages: Transition, Observation, Fusion, Reward-policy construction, and Action (TOFRA). The TOFRA framework serves to synthesize the current state of the art, provide a critical review of releva"},"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":"2508.15354","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-08-21T08:33:51Z","cross_cats_sorted":[],"title_canon_sha256":"527c590bfdcf715e1a18997e776f5c72057a1231fce0d8cb9cf5699c8e6b8d8e","abstract_canon_sha256":"75b758f5ea775f3567de13fa15222b7fde330eb573169aa41cb7d5563e05fe03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:10.743828Z","signature_b64":"B+ldpvL4zUKM4vuPc0+6b0JinBdKEgGW4GvINWQxrMVgzkoN6kzp6jeg+4YI9ThKQ6Kmui1uqTnSkgZTVQbSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25691a36e51aeea9fbcd1063824cfc1df348008dd26f8904f00b147410ef23d8","last_reissued_at":"2026-07-05T11:57:10.743343Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:10.743343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Changhao Chen, Chaoran Xiong, Fangwen Yu, Ling Pei, Songpengchen Xia, Yue Wang, Yulong Huang","submitted_at":"2025-08-21T08:33:51Z","abstract_excerpt":"Embodied navigation (EN) advances traditional navigation by enabling robots to perform complex egocentric tasks through sensing, social, and motion intelligence. In contrast to classic methodologies that rely on explicit localization and pre-defined maps, EN leverages egocentric perception and human-like interaction strategies. This survey introduces a comprehensive EN formulation structured into five stages: Transition, Observation, Fusion, Reward-policy construction, and Action (TOFRA). The TOFRA framework serves to synthesize the current state of the art, provide a critical review of releva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.15354","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/2508.15354/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":"2508.15354","created_at":"2026-07-05T11:57:10.743402+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.15354v1","created_at":"2026-07-05T11:57:10.743402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.15354","created_at":"2026-07-05T11:57:10.743402+00:00"},{"alias_kind":"pith_short_12","alias_value":"EVURUNXFDLXK","created_at":"2026-07-05T11:57:10.743402+00:00"},{"alias_kind":"pith_short_16","alias_value":"EVURUNXFDLXKT66N","created_at":"2026-07-05T11:57:10.743402+00:00"},{"alias_kind":"pith_short_8","alias_value":"EVURUNXF","created_at":"2026-07-05T11:57:10.743402+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14801","citing_title":"Exploring Bottlenecks in VLM-LLM Navigation: How 3D Scene Understanding Capability Impacts Zero-Shot VLN","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX","json":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX.json","graph_json":"https://pith.science/api/pith-number/EVURUNXFDLXKT66NCBRYETH4DX/graph.json","events_json":"https://pith.science/api/pith-number/EVURUNXFDLXKT66NCBRYETH4DX/events.json","paper":"https://pith.science/paper/EVURUNXF"},"agent_actions":{"view_html":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX","download_json":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX.json","view_paper":"https://pith.science/paper/EVURUNXF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.15354&json=true","fetch_graph":"https://pith.science/api/pith-number/EVURUNXFDLXKT66NCBRYETH4DX/graph.json","fetch_events":"https://pith.science/api/pith-number/EVURUNXFDLXKT66NCBRYETH4DX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX/action/storage_attestation","attest_author":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX/action/author_attestation","sign_citation":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX/action/citation_signature","submit_replication":"https://pith.science/pith/EVURUNXFDLXKT66NCBRYETH4DX/action/replication_record"}},"created_at":"2026-07-05T11:57:10.743402+00:00","updated_at":"2026-07-05T11:57:10.743402+00:00"}