{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:MRS6LEVIY7ZUN5X2Q2HHEGKEGY","short_pith_number":"pith:MRS6LEVI","canonical_record":{"source":{"id":"2006.09001","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-06-16T08:58:07Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"11630fe6317a78d4fc425e08915f6ef01e257b4c63dfe8ba4061c9ef80b79e6a","abstract_canon_sha256":"36dcda85f919732499229d77f6399e1ac80e4b47ee5d9b49f0628fa0a5a664bb"},"schema_version":"1.0"},"canonical_sha256":"6465e592a8c7f346f6fa868e721944362bf6ac5e381583fb9f5f28dea45254f1","source":{"kind":"arxiv","id":"2006.09001","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09001","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09001v1","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09001","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"pith_short_12","alias_value":"MRS6LEVIY7ZU","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"pith_short_16","alias_value":"MRS6LEVIY7ZUN5X2","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"pith_short_8","alias_value":"MRS6LEVI","created_at":"2026-07-05T01:10:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:MRS6LEVIY7ZUN5X2Q2HHEGKEGY","target":"record","payload":{"canonical_record":{"source":{"id":"2006.09001","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-06-16T08:58:07Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"11630fe6317a78d4fc425e08915f6ef01e257b4c63dfe8ba4061c9ef80b79e6a","abstract_canon_sha256":"36dcda85f919732499229d77f6399e1ac80e4b47ee5d9b49f0628fa0a5a664bb"},"schema_version":"1.0"},"canonical_sha256":"6465e592a8c7f346f6fa868e721944362bf6ac5e381583fb9f5f28dea45254f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:10:45.588233Z","signature_b64":"tCi8NhJTHGg2HqRIUSrDfmUqcrsswkRyY664N8cYu4HZdkFHQGn6rl4/zSFJto7nXnGQFh82lDnsm2JIXR+YCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6465e592a8c7f346f6fa868e721944362bf6ac5e381583fb9f5f28dea45254f1","last_reissued_at":"2026-07-05T01:10:45.587824Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:10:45.587824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.09001","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":"2026-07-05T01:10:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e4bYoixoFc/dCQ0c5VzoKTyGfbT771HzzKHAqv3Rgs+ALoJHQ5gbPH54Wkfq8N8XywiAbx4AwxKN5sM2V/plCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:23:22.206723Z"},"content_sha256":"c737946795b37b9d754fa92fc549ce84f9cc60a24697eb1e976e74d9f86915b1","schema_version":"1.0","event_id":"sha256:c737946795b37b9d754fa92fc549ce84f9cc60a24697eb1e976e74d9f86915b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:MRS6LEVIY7ZUN5X2Q2HHEGKEGY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Alex Irpan, Chris Harris, Julian Ibarz, Kanishka Rao, Mohi Khansari, Sergey Levine","submitted_at":"2020-06-16T08:58:07Z","abstract_excerpt":"Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manually engineering or prior learning a perception system. However, data for RL is collected via running an agent in the desired environment, and for applications like robotics, running a robot in the real world may be extremely costly and time consuming. Simulated training offers an appealing alternative, but ensuring that policies trained in simulation can transfer effectively into the real world requires additional mach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09001","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/2006.09001/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":"2026-07-05T01:10:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PaUphhi5LtkRvaH7ISrCSVqvU4cGcVTgo+EuP8gCtaF4KwsBo94hAkq56A9bT/lqj0aX7nxE6wHlYM+zELqMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:23:22.207244Z"},"content_sha256":"645976f10afcf82b1353c2a6d29419a6c8aa59106fc9e3abed09b67726cb6118","schema_version":"1.0","event_id":"sha256:645976f10afcf82b1353c2a6d29419a6c8aa59106fc9e3abed09b67726cb6118"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY/bundle.json","state_url":"https://pith.science/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY/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":"2026-08-09T00:23:22Z","links":{"resolver":"https://pith.science/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY","bundle":"https://pith.science/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY/bundle.json","state":"https://pith.science/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MRS6LEVIY7ZUN5X2Q2HHEGKEGY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:MRS6LEVIY7ZUN5X2Q2HHEGKEGY","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":"36dcda85f919732499229d77f6399e1ac80e4b47ee5d9b49f0628fa0a5a664bb","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-06-16T08:58:07Z","title_canon_sha256":"11630fe6317a78d4fc425e08915f6ef01e257b4c63dfe8ba4061c9ef80b79e6a"},"schema_version":"1.0","source":{"id":"2006.09001","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09001","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09001v1","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09001","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"pith_short_12","alias_value":"MRS6LEVIY7ZU","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"pith_short_16","alias_value":"MRS6LEVIY7ZUN5X2","created_at":"2026-07-05T01:10:45Z"},{"alias_kind":"pith_short_8","alias_value":"MRS6LEVI","created_at":"2026-07-05T01:10:45Z"}],"graph_snapshots":[{"event_id":"sha256:645976f10afcf82b1353c2a6d29419a6c8aa59106fc9e3abed09b67726cb6118","target":"graph","created_at":"2026-07-05T01:10:45Z","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/2006.09001/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manually engineering or prior learning a perception system. However, data for RL is collected via running an agent in the desired environment, and for applications like robotics, running a robot in the real world may be extremely costly and time consuming. Simulated training offers an appealing alternative, but ensuring that policies trained in simulation can transfer effectively into the real world requires additional mach","authors_text":"Alex Irpan, Chris Harris, Julian Ibarz, Kanishka Rao, Mohi Khansari, Sergey Levine","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-06-16T08:58:07Z","title":"RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09001","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:c737946795b37b9d754fa92fc549ce84f9cc60a24697eb1e976e74d9f86915b1","target":"record","created_at":"2026-07-05T01:10:45Z","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":"36dcda85f919732499229d77f6399e1ac80e4b47ee5d9b49f0628fa0a5a664bb","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-06-16T08:58:07Z","title_canon_sha256":"11630fe6317a78d4fc425e08915f6ef01e257b4c63dfe8ba4061c9ef80b79e6a"},"schema_version":"1.0","source":{"id":"2006.09001","kind":"arxiv","version":1}},"canonical_sha256":"6465e592a8c7f346f6fa868e721944362bf6ac5e381583fb9f5f28dea45254f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6465e592a8c7f346f6fa868e721944362bf6ac5e381583fb9f5f28dea45254f1","first_computed_at":"2026-07-05T01:10:45.587824Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:10:45.587824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tCi8NhJTHGg2HqRIUSrDfmUqcrsswkRyY664N8cYu4HZdkFHQGn6rl4/zSFJto7nXnGQFh82lDnsm2JIXR+YCg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:10:45.588233Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.09001","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c737946795b37b9d754fa92fc549ce84f9cc60a24697eb1e976e74d9f86915b1","sha256:645976f10afcf82b1353c2a6d29419a6c8aa59106fc9e3abed09b67726cb6118"],"state_sha256":"0ced9b5fe750f4784b6a1267ea46afd13793c35d58f71bacd78bc78c5fb3c886"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a8gdtVJKpSrQSTox+hj+ra4NoZwPiAt26+Q1Ud6rWiroAx6LDilTQ25tK1av84gbv8kafAqxOiDqz0f53Es3BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:23:22.213101Z","bundle_sha256":"6d98d94dcfc82526838f902853904c46d63b392290e6c42937f357e2c33e456d"}}