{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WLVU6WJKGQXJJHT2YMVEG2YG5R","short_pith_number":"pith:WLVU6WJK","schema_version":"1.0","canonical_sha256":"b2eb4f592a342e949e7ac32a436b06ec5799c1b7184bf49c1bc9f3c939a14e07","source":{"kind":"arxiv","id":"2409.00641","version":1},"attestation_state":"computed","paper":{"title":"Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Genya Ishigami, Masafumi Endo, Tatsunori Taniai","submitted_at":"2024-09-01T07:21:12Z","abstract_excerpt":"Traversability assessment of deformable terrain is vital for safe rover navigation on planetary surfaces. Machine learning (ML) is a powerful tool for traversability prediction but faces predictive uncertainty. This uncertainty leads to prediction errors, increasing the risk of wheel slips and immobilization for planetary rovers. To address this issue, we integrate principal approaches to uncertainty handling -- quantification, exploitation, and adaptation -- into a single learning and planning framework for rover navigation. The key concept is \\emph{deep probabilistic traversability}, forming"},"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":"2409.00641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-09-01T07:21:12Z","cross_cats_sorted":[],"title_canon_sha256":"2c66dfb0bd68280a32dc34d8cef9e23742e0b8e5ae0d469102f4fdc6930251cc","abstract_canon_sha256":"8b977a954398f9f4d52dc4e8c63e461eb4cf66b54376ad4d45bf40f95f3b9886"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:01:50.589968Z","signature_b64":"8yaNzaJoTZzJFFJ8vHynbCEi2dJUyKBX2ag4arvvjqp5Q+mqpQZFkub6gng1cNLweFkhH7aaoSpEmdQLUoeIAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2eb4f592a342e949e7ac32a436b06ec5799c1b7184bf49c1bc9f3c939a14e07","last_reissued_at":"2026-07-05T09:01:50.589511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:01:50.589511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Genya Ishigami, Masafumi Endo, Tatsunori Taniai","submitted_at":"2024-09-01T07:21:12Z","abstract_excerpt":"Traversability assessment of deformable terrain is vital for safe rover navigation on planetary surfaces. Machine learning (ML) is a powerful tool for traversability prediction but faces predictive uncertainty. This uncertainty leads to prediction errors, increasing the risk of wheel slips and immobilization for planetary rovers. To address this issue, we integrate principal approaches to uncertainty handling -- quantification, exploitation, and adaptation -- into a single learning and planning framework for rover navigation. The key concept is \\emph{deep probabilistic traversability}, forming"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00641","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/2409.00641/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":"2409.00641","created_at":"2026-07-05T09:01:50.589592+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.00641v1","created_at":"2026-07-05T09:01:50.589592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00641","created_at":"2026-07-05T09:01:50.589592+00:00"},{"alias_kind":"pith_short_12","alias_value":"WLVU6WJKGQXJ","created_at":"2026-07-05T09:01:50.589592+00:00"},{"alias_kind":"pith_short_16","alias_value":"WLVU6WJKGQXJJHT2","created_at":"2026-07-05T09:01:50.589592+00:00"},{"alias_kind":"pith_short_8","alias_value":"WLVU6WJK","created_at":"2026-07-05T09:01:50.589592+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28442","citing_title":"Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R","json":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R.json","graph_json":"https://pith.science/api/pith-number/WLVU6WJKGQXJJHT2YMVEG2YG5R/graph.json","events_json":"https://pith.science/api/pith-number/WLVU6WJKGQXJJHT2YMVEG2YG5R/events.json","paper":"https://pith.science/paper/WLVU6WJK"},"agent_actions":{"view_html":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R","download_json":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R.json","view_paper":"https://pith.science/paper/WLVU6WJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.00641&json=true","fetch_graph":"https://pith.science/api/pith-number/WLVU6WJKGQXJJHT2YMVEG2YG5R/graph.json","fetch_events":"https://pith.science/api/pith-number/WLVU6WJKGQXJJHT2YMVEG2YG5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R/action/storage_attestation","attest_author":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R/action/author_attestation","sign_citation":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R/action/citation_signature","submit_replication":"https://pith.science/pith/WLVU6WJKGQXJJHT2YMVEG2YG5R/action/replication_record"}},"created_at":"2026-07-05T09:01:50.589592+00:00","updated_at":"2026-07-05T09:01:50.589592+00:00"}