{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4FTBAYXLQ3RMYEXLZABFCTTI3X","short_pith_number":"pith:4FTBAYXL","schema_version":"1.0","canonical_sha256":"e1661062eb86e2cc12ebc802514e68ddc618258dade72eb693435d9600d1fa9d","source":{"kind":"arxiv","id":"2607.27922","version":1},"attestation_state":"computed","paper":{"title":"Learning Social Robot Navigation By Sensing Human Legs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alberto Vaglio, Andrea Garulli, Antonio Giannitrapani, Renato Quartullo, Tommaso Van Der Meer","submitted_at":"2026-07-30T09:35:03Z","abstract_excerpt":"Robots navigating among pedestrians typically sense their surroundings with a 2D LiDAR mounted close to the ground. At that height, the sensor mostly sees moving legs rather than whole people, yet most learning-based navigation methods still treat pedestrians as simple shapes like circles. This paper addresses that gap with CALF (Convolutional Attention for Leg Features), an end-to-end neural architecture that combines convolutional layers, attention, and MLP to interpret leg motion directly from LiDAR scans and produce safe navigation commands. The CALF policy is trained using deep reinforcem"},"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":"2607.27922","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-30T09:35:03Z","cross_cats_sorted":[],"title_canon_sha256":"8ea637b41d6c1ad189271c19d3c3f4fad3bd1371fc2226c209e7c794ad25fcff","abstract_canon_sha256":"232076d9f342ae086db595c657dc53fae0ac12c94e4d2d1863bab99eb57a15b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1661062eb86e2cc12ebc802514e68ddc618258dade72eb693435d9600d1fa9d","last_reissued_at":"2026-07-31T01:34:48.499295Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:34:48.499295Z"},"graph_snapshot":{"paper":{"title":"Learning Social Robot Navigation By Sensing Human Legs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Alberto Vaglio, Andrea Garulli, Antonio Giannitrapani, Renato Quartullo, Tommaso Van Der Meer","submitted_at":"2026-07-30T09:35:03Z","abstract_excerpt":"Robots navigating among pedestrians typically sense their surroundings with a 2D LiDAR mounted close to the ground. At that height, the sensor mostly sees moving legs rather than whole people, yet most learning-based navigation methods still treat pedestrians as simple shapes like circles. This paper addresses that gap with CALF (Convolutional Attention for Leg Features), an end-to-end neural architecture that combines convolutional layers, attention, and MLP to interpret leg motion directly from LiDAR scans and produce safe navigation commands. The CALF policy is trained using deep reinforcem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27922","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/2607.27922/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":"2607.27922","created_at":"2026-07-31T01:34:48.502504+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.27922v1","created_at":"2026-07-31T01:34:48.502504+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27922","created_at":"2026-07-31T01:34:48.502504+00:00"},{"alias_kind":"pith_short_12","alias_value":"4FTBAYXLQ3RM","created_at":"2026-07-31T01:34:48.502504+00:00"},{"alias_kind":"pith_short_16","alias_value":"4FTBAYXLQ3RMYEXL","created_at":"2026-07-31T01:34:48.502504+00:00"},{"alias_kind":"pith_short_8","alias_value":"4FTBAYXL","created_at":"2026-07-31T01:34:48.502504+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/4FTBAYXLQ3RMYEXLZABFCTTI3X","json":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X.json","graph_json":"https://pith.science/api/pith-number/4FTBAYXLQ3RMYEXLZABFCTTI3X/graph.json","events_json":"https://pith.science/api/pith-number/4FTBAYXLQ3RMYEXLZABFCTTI3X/events.json","paper":"https://pith.science/paper/4FTBAYXL"},"agent_actions":{"view_html":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X","download_json":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X.json","view_paper":"https://pith.science/paper/4FTBAYXL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.27922&json=true","fetch_graph":"https://pith.science/api/pith-number/4FTBAYXLQ3RMYEXLZABFCTTI3X/graph.json","fetch_events":"https://pith.science/api/pith-number/4FTBAYXLQ3RMYEXLZABFCTTI3X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X/action/storage_attestation","attest_author":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X/action/author_attestation","sign_citation":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X/action/citation_signature","submit_replication":"https://pith.science/pith/4FTBAYXLQ3RMYEXLZABFCTTI3X/action/replication_record"}},"created_at":"2026-07-31T01:34:48.502504+00:00","updated_at":"2026-07-31T01:34:48.502504+00:00"}