{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:S5QDR7BPA6HMAFKR4WLUOJTGE6","short_pith_number":"pith:S5QDR7BP","schema_version":"1.0","canonical_sha256":"976038fc2f078ec01551e5974726662781fe2b51de5ff24c19a46b234ddca57b","source":{"kind":"arxiv","id":"2110.09470","version":2},"attestation_state":"computed","paper":{"title":"No RL, No Simulation: Learning to Navigate without Navigating","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhinav Gupta, Devendra Chaplot, James M. Rehg, Meera Hahn, Mustafa Mukadam, Shubham Tulsiani","submitted_at":"2021-10-18T17:04:06Z","abstract_excerpt":"Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards. However, building simulators is expensive (requires manual effort for each and every scene) and creates challenges in transferring learned policies to robotic platforms in the real-world, due to the sim-to-real domain gap. In this paper, we pose a simple question: Do we really need active interaction, ground-truth maps or even reinforcement-learning (RL) in order to solve the image-goal navigation task? We propose a sel"},"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":"2110.09470","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-10-18T17:04:06Z","cross_cats_sorted":[],"title_canon_sha256":"58c43b68ebd548c62b1a0f2d9a0892172a9012cbde56f94ebaa6893c6df89cf7","abstract_canon_sha256":"ab56fbae4512785e9d4f36ab8aab78a9aaeb3049576645c9cee6c9dc6b44f440"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:24:45.749611Z","signature_b64":"iUayek/MFHkm7wqhCmyuojgLBfjMpThZoAw9qd5RA6AXRi96p6qAB4TCnCrbVbluF56uX0gUTj93zsiv8Zs6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"976038fc2f078ec01551e5974726662781fe2b51de5ff24c19a46b234ddca57b","last_reissued_at":"2026-07-05T03:24:45.748975Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:24:45.748975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"No RL, No Simulation: Learning to Navigate without Navigating","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhinav Gupta, Devendra Chaplot, James M. Rehg, Meera Hahn, Mustafa Mukadam, Shubham Tulsiani","submitted_at":"2021-10-18T17:04:06Z","abstract_excerpt":"Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards. However, building simulators is expensive (requires manual effort for each and every scene) and creates challenges in transferring learned policies to robotic platforms in the real-world, due to the sim-to-real domain gap. In this paper, we pose a simple question: Do we really need active interaction, ground-truth maps or even reinforcement-learning (RL) in order to solve the image-goal navigation task? We propose a sel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09470","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/2110.09470/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":"2110.09470","created_at":"2026-07-05T03:24:45.749066+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.09470v2","created_at":"2026-07-05T03:24:45.749066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09470","created_at":"2026-07-05T03:24:45.749066+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5QDR7BPA6HM","created_at":"2026-07-05T03:24:45.749066+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5QDR7BPA6HMAFKR","created_at":"2026-07-05T03:24:45.749066+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5QDR7BP","created_at":"2026-07-05T03:24:45.749066+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/S5QDR7BPA6HMAFKR4WLUOJTGE6","json":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6.json","graph_json":"https://pith.science/api/pith-number/S5QDR7BPA6HMAFKR4WLUOJTGE6/graph.json","events_json":"https://pith.science/api/pith-number/S5QDR7BPA6HMAFKR4WLUOJTGE6/events.json","paper":"https://pith.science/paper/S5QDR7BP"},"agent_actions":{"view_html":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6","download_json":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6.json","view_paper":"https://pith.science/paper/S5QDR7BP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.09470&json=true","fetch_graph":"https://pith.science/api/pith-number/S5QDR7BPA6HMAFKR4WLUOJTGE6/graph.json","fetch_events":"https://pith.science/api/pith-number/S5QDR7BPA6HMAFKR4WLUOJTGE6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6/action/storage_attestation","attest_author":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6/action/author_attestation","sign_citation":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6/action/citation_signature","submit_replication":"https://pith.science/pith/S5QDR7BPA6HMAFKR4WLUOJTGE6/action/replication_record"}},"created_at":"2026-07-05T03:24:45.749066+00:00","updated_at":"2026-07-05T03:24:45.749066+00:00"}