{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LGWT6OJ2H35U2BJ5EWCJ6YMPQ5","short_pith_number":"pith:LGWT6OJ2","canonical_record":{"source":{"id":"1807.05211","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-11T11:05:12Z","cross_cats_sorted":[],"title_canon_sha256":"5b5277643766f270eb97381cfbe563cb227b40ba934c8ef1f305af10df997f60","abstract_canon_sha256":"f515cf1838421972b3d2bfb134b110db65f51a5eeb7352e588a17f336f894e82"},"schema_version":"1.0"},"canonical_sha256":"59ad3f393a3efb4d053d25849f618f876ae79ae12903f6765dbe2ad1d5b18d31","source":{"kind":"arxiv","id":"1807.05211","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05211","created_at":"2026-05-18T00:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05211v1","created_at":"2026-05-18T00:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05211","created_at":"2026-05-18T00:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"LGWT6OJ2H35U","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LGWT6OJ2H35U2BJ5","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LGWT6OJ2","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LGWT6OJ2H35U2BJ5EWCJ6YMPQ5","target":"record","payload":{"canonical_record":{"source":{"id":"1807.05211","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-11T11:05:12Z","cross_cats_sorted":[],"title_canon_sha256":"5b5277643766f270eb97381cfbe563cb227b40ba934c8ef1f305af10df997f60","abstract_canon_sha256":"f515cf1838421972b3d2bfb134b110db65f51a5eeb7352e588a17f336f894e82"},"schema_version":"1.0"},"canonical_sha256":"59ad3f393a3efb4d053d25849f618f876ae79ae12903f6765dbe2ad1d5b18d31","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:44.094705Z","signature_b64":"j4LPmjB20CUvOkIP/5WRvKf6LwWFByNvEH8OomLP242cyApYN7ift60SBnFmlqQEz/oI2N9SVNz2btzPLYfBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59ad3f393a3efb4d053d25849f618f876ae79ae12903f6765dbe2ad1d5b18d31","last_reissued_at":"2026-05-18T00:10:44.094193Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:44.094193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.05211","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MoL1Sjd9BvoWCbKfRhcQrJtAn/FpbCwyo+fNfo6p4GBwp19jF/N/YZBH4k+JrBADErFyFR1zQhiiyJjmULXRBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:47:19.667972Z"},"content_sha256":"263bec618ae362f608ac78cc1910d69a833ab8ff7ab9a523ea9a0677fddb23f7","schema_version":"1.0","event_id":"sha256:263bec618ae362f608ac78cc1910d69a833ab8ff7ab9a523ea9a0677fddb23f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LGWT6OJ2H35U2BJ5EWCJ6YMPQ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jake Bruce, Michael Milford, Niko S\\\"underhauf, Piotr Mirowski, Raia Hadsell","submitted_at":"2018-07-11T11:05:12Z","abstract_excerpt":"Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05211","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nKUFOM3ev6hEJixLpITG7nxdGR7Cd8xD5NVUrAdioJJ8A9BqnkMNl/ioQyRzsgLK2NPzqXdaMxTQ2E3stq65AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:47:19.668641Z"},"content_sha256":"09ba290e6555cacdf07e44a6ab3d87fed4d494e79ac630558e0a0a0787b4b060","schema_version":"1.0","event_id":"sha256:09ba290e6555cacdf07e44a6ab3d87fed4d494e79ac630558e0a0a0787b4b060"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5/bundle.json","state_url":"https://pith.science/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T19:47:19Z","links":{"resolver":"https://pith.science/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5","bundle":"https://pith.science/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5/bundle.json","state":"https://pith.science/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LGWT6OJ2H35U2BJ5EWCJ6YMPQ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LGWT6OJ2H35U2BJ5EWCJ6YMPQ5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f515cf1838421972b3d2bfb134b110db65f51a5eeb7352e588a17f336f894e82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-11T11:05:12Z","title_canon_sha256":"5b5277643766f270eb97381cfbe563cb227b40ba934c8ef1f305af10df997f60"},"schema_version":"1.0","source":{"id":"1807.05211","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05211","created_at":"2026-05-18T00:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05211v1","created_at":"2026-05-18T00:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05211","created_at":"2026-05-18T00:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"LGWT6OJ2H35U","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LGWT6OJ2H35U2BJ5","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LGWT6OJ2","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:09ba290e6555cacdf07e44a6ab3d87fed4d494e79ac630558e0a0a0787b4b060","target":"graph","created_at":"2026-05-18T00:10:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of t","authors_text":"Jake Bruce, Michael Milford, Niko S\\\"underhauf, Piotr Mirowski, Raia Hadsell","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-11T11:05:12Z","title":"Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05211","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:263bec618ae362f608ac78cc1910d69a833ab8ff7ab9a523ea9a0677fddb23f7","target":"record","created_at":"2026-05-18T00:10:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f515cf1838421972b3d2bfb134b110db65f51a5eeb7352e588a17f336f894e82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-11T11:05:12Z","title_canon_sha256":"5b5277643766f270eb97381cfbe563cb227b40ba934c8ef1f305af10df997f60"},"schema_version":"1.0","source":{"id":"1807.05211","kind":"arxiv","version":1}},"canonical_sha256":"59ad3f393a3efb4d053d25849f618f876ae79ae12903f6765dbe2ad1d5b18d31","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59ad3f393a3efb4d053d25849f618f876ae79ae12903f6765dbe2ad1d5b18d31","first_computed_at":"2026-05-18T00:10:44.094193Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:44.094193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j4LPmjB20CUvOkIP/5WRvKf6LwWFByNvEH8OomLP242cyApYN7ift60SBnFmlqQEz/oI2N9SVNz2btzPLYfBCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:44.094705Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.05211","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:263bec618ae362f608ac78cc1910d69a833ab8ff7ab9a523ea9a0677fddb23f7","sha256:09ba290e6555cacdf07e44a6ab3d87fed4d494e79ac630558e0a0a0787b4b060"],"state_sha256":"be336255eafe1c41c935f90f309a6aecc6905c6e7db586b537df3e7932881b76"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EUI+EMPUxzWwaExpk9fMXo6b/32DXbiy+S1I9hkVQEKO4aTnZhbhUnnOvxKwTSIBARD9IzLoVs1bOXnG41Z2Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T19:47:19.672453Z","bundle_sha256":"c2c8a5b0e9358ae061d054e8afcc53dba4f42331a90b09cb851cfa41610ac33b"}}