{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:K7F5H6XJB5EYSJZ55FFHXVZ3CH","short_pith_number":"pith:K7F5H6XJ","schema_version":"1.0","canonical_sha256":"57cbd3fae90f4989273de94a7bd73b11e2349a5d4947e95b715b24b42273b130","source":{"kind":"arxiv","id":"2506.04484","version":2},"attestation_state":"computed","paper":{"title":"Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adam J. Thorpe, Christian Ellis, Sarah Etter, Tyler Ingebrand, Ufuk Topcu, William Ward","submitted_at":"2025-06-04T22:03:57Z","abstract_excerpt":"Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a critical challenge becomes estimating the robot's dynamics on changing terrain in order to enable reliable, accurate navigation and planning. We present a novel online adaptation approach for terrain-aware dynamics modeling and planning using function encoders. Our approach efficiently adapts to new terrains at runtime using limited online data without retraining or fine-tuning. By learning a set of neural network basis"},"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":"2506.04484","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-04T22:03:57Z","cross_cats_sorted":[],"title_canon_sha256":"b9a2246a18f7354dc339b61e49ba054509c55c3209f96a2cb9419558fa009cd9","abstract_canon_sha256":"02bf6d7af11c5e117f822e1d676a171757d5fb6d57bcfcbf8ff849f6eac9eaf8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:29.998082Z","signature_b64":"e24MJD1UujmKiF65QXcmf73S2mL4we6IH7+rQMpjdtBzfqbQBFucuq7jn6d4HzQG3Rrq0kHvccfmLbitFdlzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57cbd3fae90f4989273de94a7bd73b11e2349a5d4947e95b715b24b42273b130","last_reissued_at":"2026-07-05T11:38:29.997509Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:29.997509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adam J. Thorpe, Christian Ellis, Sarah Etter, Tyler Ingebrand, Ufuk Topcu, William Ward","submitted_at":"2025-06-04T22:03:57Z","abstract_excerpt":"Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a critical challenge becomes estimating the robot's dynamics on changing terrain in order to enable reliable, accurate navigation and planning. We present a novel online adaptation approach for terrain-aware dynamics modeling and planning using function encoders. Our approach efficiently adapts to new terrains at runtime using limited online data without retraining or fine-tuning. By learning a set of neural network basis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04484","kind":"arxiv","version":2},"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/2506.04484/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":"2506.04484","created_at":"2026-07-05T11:38:29.997573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04484v2","created_at":"2026-07-05T11:38:29.997573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04484","created_at":"2026-07-05T11:38:29.997573+00:00"},{"alias_kind":"pith_short_12","alias_value":"K7F5H6XJB5EY","created_at":"2026-07-05T11:38:29.997573+00:00"},{"alias_kind":"pith_short_16","alias_value":"K7F5H6XJB5EYSJZ5","created_at":"2026-07-05T11:38:29.997573+00:00"},{"alias_kind":"pith_short_8","alias_value":"K7F5H6XJ","created_at":"2026-07-05T11:38:29.997573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00673","citing_title":"Path Planning in Physically Viable World Models","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH","json":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH.json","graph_json":"https://pith.science/api/pith-number/K7F5H6XJB5EYSJZ55FFHXVZ3CH/graph.json","events_json":"https://pith.science/api/pith-number/K7F5H6XJB5EYSJZ55FFHXVZ3CH/events.json","paper":"https://pith.science/paper/K7F5H6XJ"},"agent_actions":{"view_html":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH","download_json":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH.json","view_paper":"https://pith.science/paper/K7F5H6XJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04484&json=true","fetch_graph":"https://pith.science/api/pith-number/K7F5H6XJB5EYSJZ55FFHXVZ3CH/graph.json","fetch_events":"https://pith.science/api/pith-number/K7F5H6XJB5EYSJZ55FFHXVZ3CH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH/action/storage_attestation","attest_author":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH/action/author_attestation","sign_citation":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH/action/citation_signature","submit_replication":"https://pith.science/pith/K7F5H6XJB5EYSJZ55FFHXVZ3CH/action/replication_record"}},"created_at":"2026-07-05T11:38:29.997573+00:00","updated_at":"2026-07-05T11:38:29.997573+00:00"}