{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OOOT3QQZ46CT4B7IDNTRU5RLR4","short_pith_number":"pith:OOOT3QQZ","schema_version":"1.0","canonical_sha256":"739d3dc219e7853e07e81b671a762b8f295a9b0e8972b3964fe50d228e7e2949","source":{"kind":"arxiv","id":"1903.01292","version":1},"attestation_state":"computed","paper":{"title":"The StreetLearn Environment and Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.RO"],"primary_cat":"cs.AI","authors_text":"Andras Banki-Horvath, Andrew Zisserman, Denis Teplyashin, Karen Simonyan, Karl Moritz Hermann, Keith Anderson, Koray Kavukcuoglu, Mateusz Malinowski, Matthew Koichi Grimes, Piotr Mirowski, Raia Hadsell","submitted_at":"2019-03-04T16:21:22Z","abstract_excerpt":"Navigation is a rich and well-grounded problem domain that drives progress in many different areas of research: perception, planning, memory, exploration, and optimisation in particular. Historically these challenges have been separately considered and solutions built that rely on stationary datasets - for example, recorded trajectories through an environment. These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simulta"},"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":"1903.01292","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-03-04T16:21:22Z","cross_cats_sorted":["cs.CV","cs.RO"],"title_canon_sha256":"0964cffc286be039b3a21ae6f1083c901a533b08285386b6eaf4c832c0d3e443","abstract_canon_sha256":"b4328246a3cfe3d4938ccea58aac55b21149b5d062a3fbb20c9bd7179912c86b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:00.597959Z","signature_b64":"UqoqyHeBUZhc7LJZDOCmJWrlE6AS9+yPu4RiKY1gR/Pq1K0ZFrVogXCgC0dAm0T9c6eesrU25EC4nbo1Q44KAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"739d3dc219e7853e07e81b671a762b8f295a9b0e8972b3964fe50d228e7e2949","last_reissued_at":"2026-05-17T23:52:00.597280Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:00.597280Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The StreetLearn Environment and Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.RO"],"primary_cat":"cs.AI","authors_text":"Andras Banki-Horvath, Andrew Zisserman, Denis Teplyashin, Karen Simonyan, Karl Moritz Hermann, Keith Anderson, Koray Kavukcuoglu, Mateusz Malinowski, Matthew Koichi Grimes, Piotr Mirowski, Raia Hadsell","submitted_at":"2019-03-04T16:21:22Z","abstract_excerpt":"Navigation is a rich and well-grounded problem domain that drives progress in many different areas of research: perception, planning, memory, exploration, and optimisation in particular. Historically these challenges have been separately considered and solutions built that rely on stationary datasets - for example, recorded trajectories through an environment. These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simulta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.01292","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":"1903.01292","created_at":"2026-05-17T23:52:00.597403+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.01292v1","created_at":"2026-05-17T23:52:00.597403+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.01292","created_at":"2026-05-17T23:52:00.597403+00:00"},{"alias_kind":"pith_short_12","alias_value":"OOOT3QQZ46CT","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"OOOT3QQZ46CT4B7I","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"OOOT3QQZ","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2512.12165","citing_title":"Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video","ref_index":35,"is_internal_anchor":true},{"citing_arxiv_id":"2603.13200","citing_title":"Navig-AI-tion: Navigation by Contextual AI and Spatial Audio","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07705","citing_title":"Vision-Language Navigation for Aerial Robots: Towards the Era of Large Language Models","ref_index":51,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4","json":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4.json","graph_json":"https://pith.science/api/pith-number/OOOT3QQZ46CT4B7IDNTRU5RLR4/graph.json","events_json":"https://pith.science/api/pith-number/OOOT3QQZ46CT4B7IDNTRU5RLR4/events.json","paper":"https://pith.science/paper/OOOT3QQZ"},"agent_actions":{"view_html":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4","download_json":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4.json","view_paper":"https://pith.science/paper/OOOT3QQZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.01292&json=true","fetch_graph":"https://pith.science/api/pith-number/OOOT3QQZ46CT4B7IDNTRU5RLR4/graph.json","fetch_events":"https://pith.science/api/pith-number/OOOT3QQZ46CT4B7IDNTRU5RLR4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4/action/storage_attestation","attest_author":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4/action/author_attestation","sign_citation":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4/action/citation_signature","submit_replication":"https://pith.science/pith/OOOT3QQZ46CT4B7IDNTRU5RLR4/action/replication_record"}},"created_at":"2026-05-17T23:52:00.597403+00:00","updated_at":"2026-05-17T23:52:00.597403+00:00"}