{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:43CERRZNTC3QSOGETIZ6JKJAXW","short_pith_number":"pith:43CERRZN","schema_version":"1.0","canonical_sha256":"e6c448c72d98b70938c49a33e4a920bd93c21654c4ee0b4fb478fafad3ffdcb1","source":{"kind":"arxiv","id":"2407.01593","version":1},"attestation_state":"computed","paper":{"title":"neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Luca Castri, Marc Hanheide, Nicola Bellotto, Sariah Mghames","submitted_at":"2024-06-24T11:13:06Z","abstract_excerpt":"Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neuro-symbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatia"},"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":"2407.01593","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-06-24T11:13:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e89699a9a2a307f259170a863dbbf6ef258a20257d25285da567ca3281cf4552","abstract_canon_sha256":"b11451be6ec880c2d58f16171ecacca4fa136b84fc4e2be7f99c7e9725f00ebf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:02.509473Z","signature_b64":"wWH0D7s3zWYA9rQDuvcciX8hXYVKldUyD15zgaYWMr5CshKcx3nC3FLeAcSixy+hyoRxAXsY26jRzapr0BfHDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6c448c72d98b70938c49a33e4a920bd93c21654c4ee0b4fb478fafad3ffdcb1","last_reissued_at":"2026-07-05T08:39:02.509076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:02.509076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Luca Castri, Marc Hanheide, Nicola Bellotto, Sariah Mghames","submitted_at":"2024-06-24T11:13:06Z","abstract_excerpt":"Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neuro-symbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01593","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/2407.01593/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":"2407.01593","created_at":"2026-07-05T08:39:02.509132+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01593v1","created_at":"2026-07-05T08:39:02.509132+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01593","created_at":"2026-07-05T08:39:02.509132+00:00"},{"alias_kind":"pith_short_12","alias_value":"43CERRZNTC3Q","created_at":"2026-07-05T08:39:02.509132+00:00"},{"alias_kind":"pith_short_16","alias_value":"43CERRZNTC3QSOGE","created_at":"2026-07-05T08:39:02.509132+00:00"},{"alias_kind":"pith_short_8","alias_value":"43CERRZN","created_at":"2026-07-05T08:39:02.509132+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW","json":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW.json","graph_json":"https://pith.science/api/pith-number/43CERRZNTC3QSOGETIZ6JKJAXW/graph.json","events_json":"https://pith.science/api/pith-number/43CERRZNTC3QSOGETIZ6JKJAXW/events.json","paper":"https://pith.science/paper/43CERRZN"},"agent_actions":{"view_html":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW","download_json":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW.json","view_paper":"https://pith.science/paper/43CERRZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01593&json=true","fetch_graph":"https://pith.science/api/pith-number/43CERRZNTC3QSOGETIZ6JKJAXW/graph.json","fetch_events":"https://pith.science/api/pith-number/43CERRZNTC3QSOGETIZ6JKJAXW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW/action/storage_attestation","attest_author":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW/action/author_attestation","sign_citation":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW/action/citation_signature","submit_replication":"https://pith.science/pith/43CERRZNTC3QSOGETIZ6JKJAXW/action/replication_record"}},"created_at":"2026-07-05T08:39:02.509132+00:00","updated_at":"2026-07-05T08:39:02.509132+00:00"}