{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TJBQ2B2FFJAYBA4SGGOHQ5WQUA","short_pith_number":"pith:TJBQ2B2F","schema_version":"1.0","canonical_sha256":"9a430d07452a41808392319c7876d0a01d3401b0b36425eb09bd745c4c03225c","source":{"kind":"arxiv","id":"2503.04579","version":1},"attestation_state":"computed","paper":{"title":"Data-augmented Learning of Geodesic Distances in Irregular Domains through Soner Boundary Conditions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Florian T. Pokorny, Rafael I. Cabral Muchacho","submitted_at":"2025-03-06T16:13:32Z","abstract_excerpt":"Geodesic distances play a fundamental role in robotics, as they efficiently encode global geometric information of the domain. Recent methods use neural networks to approximate geodesic distances by solving the Eikonal equation through physics-informed approaches. While effective, these approaches often suffer from unstable convergence during training in complex environments. We propose a framework to learn geodesic distances in irregular domains by using the Soner boundary condition, and systematically evaluate the impact of data losses on training stability and solution accuracy. Our experim"},"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":"2503.04579","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-06T16:13:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"18dda7b8e1e8cf3daa6e8985576fa6291a2c2e145412d5e050b8da2b50fa96b7","abstract_canon_sha256":"6835859cf1a6931287e92d452ee7c4253c8bef5b44b066f476c38f5e6c4a8b94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:25:42.572609Z","signature_b64":"8l99z/U0ko/mTdVfH7UJBb829M4Tth1PCxYllx5+iXURims8lSzFi0ULBo9Beexa2NGDqh8iKYH8maA2GBTCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a430d07452a41808392319c7876d0a01d3401b0b36425eb09bd745c4c03225c","last_reissued_at":"2026-07-05T10:25:42.571493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:25:42.571493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-augmented Learning of Geodesic Distances in Irregular Domains through Soner Boundary Conditions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Florian T. Pokorny, Rafael I. Cabral Muchacho","submitted_at":"2025-03-06T16:13:32Z","abstract_excerpt":"Geodesic distances play a fundamental role in robotics, as they efficiently encode global geometric information of the domain. Recent methods use neural networks to approximate geodesic distances by solving the Eikonal equation through physics-informed approaches. While effective, these approaches often suffer from unstable convergence during training in complex environments. We propose a framework to learn geodesic distances in irregular domains by using the Soner boundary condition, and systematically evaluate the impact of data losses on training stability and solution accuracy. Our experim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04579","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/2503.04579/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":"2503.04579","created_at":"2026-07-05T10:25:42.571611+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.04579v1","created_at":"2026-07-05T10:25:42.571611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04579","created_at":"2026-07-05T10:25:42.571611+00:00"},{"alias_kind":"pith_short_12","alias_value":"TJBQ2B2FFJAY","created_at":"2026-07-05T10:25:42.571611+00:00"},{"alias_kind":"pith_short_16","alias_value":"TJBQ2B2FFJAYBA4S","created_at":"2026-07-05T10:25:42.571611+00:00"},{"alias_kind":"pith_short_8","alias_value":"TJBQ2B2F","created_at":"2026-07-05T10:25:42.571611+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/TJBQ2B2FFJAYBA4SGGOHQ5WQUA","json":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA.json","graph_json":"https://pith.science/api/pith-number/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/graph.json","events_json":"https://pith.science/api/pith-number/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/events.json","paper":"https://pith.science/paper/TJBQ2B2F"},"agent_actions":{"view_html":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA","download_json":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA.json","view_paper":"https://pith.science/paper/TJBQ2B2F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.04579&json=true","fetch_graph":"https://pith.science/api/pith-number/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/graph.json","fetch_events":"https://pith.science/api/pith-number/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/action/storage_attestation","attest_author":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/action/author_attestation","sign_citation":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/action/citation_signature","submit_replication":"https://pith.science/pith/TJBQ2B2FFJAYBA4SGGOHQ5WQUA/action/replication_record"}},"created_at":"2026-07-05T10:25:42.571611+00:00","updated_at":"2026-07-05T10:25:42.571611+00:00"}