{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JCITN6IRRNL5YGBDRYDUFDDXG5","short_pith_number":"pith:JCITN6IR","schema_version":"1.0","canonical_sha256":"489136f9118b57dc18238e07428c77375ae47a0201a28cf981549aa8884657cd","source":{"kind":"arxiv","id":"1901.05599","version":1},"attestation_state":"computed","paper":{"title":"Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Daniel Szafir, Michael E. Walker, Michael L. Iuzzolino","submitted_at":"2019-01-17T03:11:58Z","abstract_excerpt":"Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, espec"},"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":"1901.05599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-17T03:11:58Z","cross_cats_sorted":["cs.CV","cs.RO","stat.ML"],"title_canon_sha256":"4bd638cbcfad51b3060a6017e713e75d7dd02b59594751a81e17f22e448ee70a","abstract_canon_sha256":"f8e105b0bb5b349a910c64bec27c0e6a536d42a98fc3a82d8716dd5965cc1409"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:35.057588Z","signature_b64":"vpIz6elcQJsVI0RvXduTY7aLhU/9HPdEVOfMk+88FqfouvUajS7ll4lfQs5qYTwbF29HfbBvoHzIV9uR9T95Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"489136f9118b57dc18238e07428c77375ae47a0201a28cf981549aa8884657cd","last_reissued_at":"2026-05-17T23:55:35.057108Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:35.057108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Daniel Szafir, Michael E. Walker, Michael L. Iuzzolino","submitted_at":"2019-01-17T03:11:58Z","abstract_excerpt":"Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, espec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05599","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":""},"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":"1901.05599","created_at":"2026-05-17T23:55:35.057171+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.05599v1","created_at":"2026-05-17T23:55:35.057171+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05599","created_at":"2026-05-17T23:55:35.057171+00:00"},{"alias_kind":"pith_short_12","alias_value":"JCITN6IRRNL5","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"JCITN6IRRNL5YGBD","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"JCITN6IR","created_at":"2026-05-18T12:33:18.533446+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/JCITN6IRRNL5YGBDRYDUFDDXG5","json":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5.json","graph_json":"https://pith.science/api/pith-number/JCITN6IRRNL5YGBDRYDUFDDXG5/graph.json","events_json":"https://pith.science/api/pith-number/JCITN6IRRNL5YGBDRYDUFDDXG5/events.json","paper":"https://pith.science/paper/JCITN6IR"},"agent_actions":{"view_html":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5","download_json":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5.json","view_paper":"https://pith.science/paper/JCITN6IR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.05599&json=true","fetch_graph":"https://pith.science/api/pith-number/JCITN6IRRNL5YGBDRYDUFDDXG5/graph.json","fetch_events":"https://pith.science/api/pith-number/JCITN6IRRNL5YGBDRYDUFDDXG5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5/action/storage_attestation","attest_author":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5/action/author_attestation","sign_citation":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5/action/citation_signature","submit_replication":"https://pith.science/pith/JCITN6IRRNL5YGBDRYDUFDDXG5/action/replication_record"}},"created_at":"2026-05-17T23:55:35.057171+00:00","updated_at":"2026-05-17T23:55:35.057171+00:00"}