{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:PZCILRV4ZW2HAQLYNX4FLJGJ7Q","short_pith_number":"pith:PZCILRV4","canonical_record":{"source":{"id":"2107.11762","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-07-25T09:15:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"446f0de0872085ce77fefe9ef3807752a65f8eacb7f0e50751c35e850c9fc367","abstract_canon_sha256":"13621d28bd9acfb3545b640129ba9f0a49c294272e2b3a52875c25c80004ff5b"},"schema_version":"1.0"},"canonical_sha256":"7e4485c6bccdb47041786df855a4c9fc3d323d31f4504ac44ba418ae7ab79523","source":{"kind":"arxiv","id":"2107.11762","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.11762","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"arxiv_version","alias_value":"2107.11762v1","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.11762","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"pith_short_12","alias_value":"PZCILRV4ZW2H","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"pith_short_16","alias_value":"PZCILRV4ZW2HAQLY","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"pith_short_8","alias_value":"PZCILRV4","created_at":"2026-07-05T03:00:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:PZCILRV4ZW2HAQLYNX4FLJGJ7Q","target":"record","payload":{"canonical_record":{"source":{"id":"2107.11762","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-07-25T09:15:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"446f0de0872085ce77fefe9ef3807752a65f8eacb7f0e50751c35e850c9fc367","abstract_canon_sha256":"13621d28bd9acfb3545b640129ba9f0a49c294272e2b3a52875c25c80004ff5b"},"schema_version":"1.0"},"canonical_sha256":"7e4485c6bccdb47041786df855a4c9fc3d323d31f4504ac44ba418ae7ab79523","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:37.291400Z","signature_b64":"XpodSI//dX5WeGvQANIy8sPkryg1pjvYa8C03P1FWOYKsJ6ggB2ORyx0O2U8fX+4WiyTktSvzknCtwhGNB5mDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e4485c6bccdb47041786df855a4c9fc3d323d31f4504ac44ba418ae7ab79523","last_reissued_at":"2026-07-05T03:00:37.291001Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:37.291001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.11762","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":"2026-07-05T03:00:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ILWf1Qi8oBAyrIrC+kp4cKOW8qe3dr11zdJh9M5WaCBUkiO9jEFvaFbPq752iX1M+wPukGuP9QBhX0gWLlNjBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:39:03.584590Z"},"content_sha256":"40e01838b1e18898dae06bd65382c57ec863a3c5d7b01c1840f651a08a5a5284","schema_version":"1.0","event_id":"sha256:40e01838b1e18898dae06bd65382c57ec863a3c5d7b01c1840f651a08a5a5284"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:PZCILRV4ZW2HAQLYNX4FLJGJ7Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Haoyi Niu, Jianming Hu, Yi Zhang, Zheyu Cui","submitted_at":"2021-07-25T09:15:46Z","abstract_excerpt":"How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving. Training with simulated data is less costly and dangerous than utilizing real-world data, but the inconsistency of parameter distribution and the incorrect system modeling in simulators always lead to an inevitable Sim2real gap, which probably accounts for the underperformance in novel, anomalous and risky cases that simulators can hardly generate. Domain Randomization(DR) is a methodology that can bridge this gap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.11762","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.11762/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:00:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GOjOt6oG/obDnwoOIFGFmNyp6gHRuzBwgpnqwyP7A0osC3sWEW5l01tdF9YL8rBYAd4vGLVe95Vf0wXy3Wl2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:39:03.584960Z"},"content_sha256":"879ef7e1aa431720d93d5ccf9e215784a0d59d1efe88edb1f3a93226be8ec15f","schema_version":"1.0","event_id":"sha256:879ef7e1aa431720d93d5ccf9e215784a0d59d1efe88edb1f3a93226be8ec15f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q/bundle.json","state_url":"https://pith.science/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q/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":"2026-07-06T18:39:03Z","links":{"resolver":"https://pith.science/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q","bundle":"https://pith.science/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q/bundle.json","state":"https://pith.science/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PZCILRV4ZW2HAQLYNX4FLJGJ7Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:PZCILRV4ZW2HAQLYNX4FLJGJ7Q","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":"13621d28bd9acfb3545b640129ba9f0a49c294272e2b3a52875c25c80004ff5b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-07-25T09:15:46Z","title_canon_sha256":"446f0de0872085ce77fefe9ef3807752a65f8eacb7f0e50751c35e850c9fc367"},"schema_version":"1.0","source":{"id":"2107.11762","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.11762","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"arxiv_version","alias_value":"2107.11762v1","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.11762","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"pith_short_12","alias_value":"PZCILRV4ZW2H","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"pith_short_16","alias_value":"PZCILRV4ZW2HAQLY","created_at":"2026-07-05T03:00:37Z"},{"alias_kind":"pith_short_8","alias_value":"PZCILRV4","created_at":"2026-07-05T03:00:37Z"}],"graph_snapshots":[{"event_id":"sha256:879ef7e1aa431720d93d5ccf9e215784a0d59d1efe88edb1f3a93226be8ec15f","target":"graph","created_at":"2026-07-05T03:00:37Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.11762/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving. Training with simulated data is less costly and dangerous than utilizing real-world data, but the inconsistency of parameter distribution and the incorrect system modeling in simulators always lead to an inevitable Sim2real gap, which probably accounts for the underperformance in novel, anomalous and risky cases that simulators can hardly generate. Domain Randomization(DR) is a methodology that can bridge this gap","authors_text":"Haoyi Niu, Jianming Hu, Yi Zhang, Zheyu Cui","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-07-25T09:15:46Z","title":"DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.11762","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":"sha256:40e01838b1e18898dae06bd65382c57ec863a3c5d7b01c1840f651a08a5a5284","target":"record","created_at":"2026-07-05T03:00:37Z","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":"13621d28bd9acfb3545b640129ba9f0a49c294272e2b3a52875c25c80004ff5b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-07-25T09:15:46Z","title_canon_sha256":"446f0de0872085ce77fefe9ef3807752a65f8eacb7f0e50751c35e850c9fc367"},"schema_version":"1.0","source":{"id":"2107.11762","kind":"arxiv","version":1}},"canonical_sha256":"7e4485c6bccdb47041786df855a4c9fc3d323d31f4504ac44ba418ae7ab79523","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e4485c6bccdb47041786df855a4c9fc3d323d31f4504ac44ba418ae7ab79523","first_computed_at":"2026-07-05T03:00:37.291001Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:00:37.291001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XpodSI//dX5WeGvQANIy8sPkryg1pjvYa8C03P1FWOYKsJ6ggB2ORyx0O2U8fX+4WiyTktSvzknCtwhGNB5mDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:00:37.291400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.11762","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:40e01838b1e18898dae06bd65382c57ec863a3c5d7b01c1840f651a08a5a5284","sha256:879ef7e1aa431720d93d5ccf9e215784a0d59d1efe88edb1f3a93226be8ec15f"],"state_sha256":"f56c5792356818b8087b2eb0d7bc4288ccec18d433b8458de9986fb0ebc5e552"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9AABdFWNwNwJ5PSSQakTzYNLukDkXc9qHwaK+B0ejp/nP7shb5Ca+DBO2w+CSV0m5kPvglbnlbaEOSrUbNqDCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:39:03.586822Z","bundle_sha256":"443c14a3bc33eb73f6d70d97c4ebca3a18d37c3c1f94e6267c5ae8b34a559e3b"}}