{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MTUWODWGODGYUWUASRP2JTGV36","short_pith_number":"pith:MTUWODWG","canonical_record":{"source":{"id":"1902.03701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T01:45:53Z","cross_cats_sorted":["cs.RO","stat.ML"],"title_canon_sha256":"b5cfb204d6ffac8ab05744f0b88b2a122cb620bf2aea32605d1a50f7e070c4df","abstract_canon_sha256":"8ddb7ab1ef62a819fad1ac0e53fba6963f06799b55e02c4e18b5360f6d538eea"},"schema_version":"1.0"},"canonical_sha256":"64e9670ec670cd8a5a80945fa4ccd5dfb68d845de441595fb1c3876d126ae131","source":{"kind":"arxiv","id":"1902.03701","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03701","created_at":"2026-05-17T23:54:19Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03701v1","created_at":"2026-05-17T23:54:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03701","created_at":"2026-05-17T23:54:19Z"},{"alias_kind":"pith_short_12","alias_value":"MTUWODWGODGY","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"MTUWODWGODGYUWUA","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"MTUWODWG","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MTUWODWGODGYUWUASRP2JTGV36","target":"record","payload":{"canonical_record":{"source":{"id":"1902.03701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T01:45:53Z","cross_cats_sorted":["cs.RO","stat.ML"],"title_canon_sha256":"b5cfb204d6ffac8ab05744f0b88b2a122cb620bf2aea32605d1a50f7e070c4df","abstract_canon_sha256":"8ddb7ab1ef62a819fad1ac0e53fba6963f06799b55e02c4e18b5360f6d538eea"},"schema_version":"1.0"},"canonical_sha256":"64e9670ec670cd8a5a80945fa4ccd5dfb68d845de441595fb1c3876d126ae131","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:19.830254Z","signature_b64":"h2jWeh7p1DgN01ulomKCQu2fMpl2Ar7EEsPpjYFP5RymNVypdx8djkxbA2fzXyHvttm7r7xwz6b0PC3dNRy8Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64e9670ec670cd8a5a80945fa4ccd5dfb68d845de441595fb1c3876d126ae131","last_reissued_at":"2026-05-17T23:54:19.829559Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:19.829559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.03701","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":"2026-05-17T23:54:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J99LdPViiTg7SPUrstOsn0WbiN5sHNfRt8bOIlD228GHPCifTnQeGr/hFR7vOUWpGxiZNqjykcz4sPoAZgnKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T13:22:26.700057Z"},"content_sha256":"9864840b65fa09c78fd6dd8957511c9819231cde1eee6bb648fb60744a787814","schema_version":"1.0","event_id":"sha256:9864840b65fa09c78fd6dd8957511c9819231cde1eee6bb648fb60744a787814"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MTUWODWGODGYUWUASRP2JTGV36","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Gregory Kahn, Katie Kang, Pieter Abbeel, Sergey Levine, Suneel Belkhale","submitted_at":"2019-02-11T01:45:53Z","abstract_excerpt":"Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03701","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":"2026-05-17T23:54:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2l9vVcexCNko4YcUkMZay6XV0BbuuJaQJXFX+HcsvGouA3sVe+zRZZks5NGdYz0wsIhK3TMP4Xz3Y2iZKM6aDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T13:22:26.700418Z"},"content_sha256":"09be4e38a5fd310eec1cc6d079a9c9287e18f06e976a00e91f7c0e90f2d3a5f4","schema_version":"1.0","event_id":"sha256:09be4e38a5fd310eec1cc6d079a9c9287e18f06e976a00e91f7c0e90f2d3a5f4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MTUWODWGODGYUWUASRP2JTGV36/bundle.json","state_url":"https://pith.science/pith/MTUWODWGODGYUWUASRP2JTGV36/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MTUWODWGODGYUWUASRP2JTGV36/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":"2026-06-11T13:22:26Z","links":{"resolver":"https://pith.science/pith/MTUWODWGODGYUWUASRP2JTGV36","bundle":"https://pith.science/pith/MTUWODWGODGYUWUASRP2JTGV36/bundle.json","state":"https://pith.science/pith/MTUWODWGODGYUWUASRP2JTGV36/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MTUWODWGODGYUWUASRP2JTGV36/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MTUWODWGODGYUWUASRP2JTGV36","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":"8ddb7ab1ef62a819fad1ac0e53fba6963f06799b55e02c4e18b5360f6d538eea","cross_cats_sorted":["cs.RO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T01:45:53Z","title_canon_sha256":"b5cfb204d6ffac8ab05744f0b88b2a122cb620bf2aea32605d1a50f7e070c4df"},"schema_version":"1.0","source":{"id":"1902.03701","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03701","created_at":"2026-05-17T23:54:19Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03701v1","created_at":"2026-05-17T23:54:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03701","created_at":"2026-05-17T23:54:19Z"},{"alias_kind":"pith_short_12","alias_value":"MTUWODWGODGY","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"MTUWODWGODGYUWUA","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"MTUWODWG","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:09be4e38a5fd310eec1cc6d079a9c9287e18f06e976a00e91f7c0e90f2d3a5f4","target":"graph","created_at":"2026-05-17T23:54:19Z","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":"Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poor","authors_text":"Gregory Kahn, Katie Kang, Pieter Abbeel, Sergey Levine, Suneel Belkhale","cross_cats":["cs.RO","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T01:45:53Z","title":"Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03701","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":"sha256:9864840b65fa09c78fd6dd8957511c9819231cde1eee6bb648fb60744a787814","target":"record","created_at":"2026-05-17T23:54:19Z","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":"8ddb7ab1ef62a819fad1ac0e53fba6963f06799b55e02c4e18b5360f6d538eea","cross_cats_sorted":["cs.RO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-11T01:45:53Z","title_canon_sha256":"b5cfb204d6ffac8ab05744f0b88b2a122cb620bf2aea32605d1a50f7e070c4df"},"schema_version":"1.0","source":{"id":"1902.03701","kind":"arxiv","version":1}},"canonical_sha256":"64e9670ec670cd8a5a80945fa4ccd5dfb68d845de441595fb1c3876d126ae131","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64e9670ec670cd8a5a80945fa4ccd5dfb68d845de441595fb1c3876d126ae131","first_computed_at":"2026-05-17T23:54:19.829559Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:19.829559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h2jWeh7p1DgN01ulomKCQu2fMpl2Ar7EEsPpjYFP5RymNVypdx8djkxbA2fzXyHvttm7r7xwz6b0PC3dNRy8Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:19.830254Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.03701","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9864840b65fa09c78fd6dd8957511c9819231cde1eee6bb648fb60744a787814","sha256:09be4e38a5fd310eec1cc6d079a9c9287e18f06e976a00e91f7c0e90f2d3a5f4"],"state_sha256":"daa9f66bed54a60c65f127fa1292618159882a08459995d456bc554ef2df7b52"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3+Q5OYyHqyTn7RJVdauDcQecJ7L8u7450EcXvGqa9W7t0/+wvE0AKrS6MmsE8YslTGG0k2F1d7fEvL4Rz8j+CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T13:22:26.702337Z","bundle_sha256":"85aee27c902f6e88dbee9fc893915500aa2741306a0bca5b787eafc872da9e23"}}