{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SG7AWKCZNG5YPOXFGUSBGRNXDA","short_pith_number":"pith:SG7AWKCZ","canonical_record":{"source":{"id":"1709.10489","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-29T16:47:14Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"296d49e8b937aeb405d2fcbd71e3ae91af930753824f2a97d682fd7fb85c3967","abstract_canon_sha256":"5ca142c66bf6a5754c7447c5f5bf6cd57fc1b190c56f72404c116f7f40b48583"},"schema_version":"1.0"},"canonical_sha256":"91be0b285969bb87bae535241345b7180a5a6de5fe122e48ff2523d875f0430c","source":{"kind":"arxiv","id":"1709.10489","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.10489","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"arxiv_version","alias_value":"1709.10489v3","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.10489","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"pith_short_12","alias_value":"SG7AWKCZNG5Y","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SG7AWKCZNG5YPOXF","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SG7AWKCZ","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SG7AWKCZNG5YPOXFGUSBGRNXDA","target":"record","payload":{"canonical_record":{"source":{"id":"1709.10489","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-29T16:47:14Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"296d49e8b937aeb405d2fcbd71e3ae91af930753824f2a97d682fd7fb85c3967","abstract_canon_sha256":"5ca142c66bf6a5754c7447c5f5bf6cd57fc1b190c56f72404c116f7f40b48583"},"schema_version":"1.0"},"canonical_sha256":"91be0b285969bb87bae535241345b7180a5a6de5fe122e48ff2523d875f0430c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:42.028311Z","signature_b64":"Ec8nVqPGAhGLBK5ZKsZADlj4mQykNXdtM3hCSjYC4SmdwzzxOIk49IAm2QmRwFtSs7pXBdzV8cX6pWLpXaRzDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91be0b285969bb87bae535241345b7180a5a6de5fe122e48ff2523d875f0430c","last_reissued_at":"2026-05-18T00:15:42.027706Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:42.027706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.10489","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g33QceH8nM0eogh/PNmJnaMd6AbrQ6y1Sv+lIstPaKqAN6KDSlesXYkpLdmg85vVCgRNiH40307fM8ScLhfaAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:09:09.915920Z"},"content_sha256":"3a5c0bc5b722bb9b9ea246682ecc9810f7dc2f984a3cf77303bed141bd94c488","schema_version":"1.0","event_id":"sha256:3a5c0bc5b722bb9b9ea246682ecc9810f7dc2f984a3cf77303bed141bd94c488"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SG7AWKCZNG5YPOXFGUSBGRNXDA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.LG","authors_text":"Adam Villaflor, Bosen Ding, Gregory Kahn, Pieter Abbeel, Sergey Levine","submitted_at":"2017-09-29T16:47:14Z","abstract_excerpt":"Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these approaches often include a variety of assumptions, are computationally intensive, and do not learn from failures. In contrast, learning-based methods improve as the robot acts in the environment, but are difficult to deploy in the real-world due to their high sample complexity. To address the need to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.10489","kind":"arxiv","version":3},"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":"2026-05-18T00:15:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O1BbFAz2bd7LCVLFF6s38R3EiahLoG+UcD7pumw6ATLx/XbiZtkdJM8P78jkYZYtQwYCblm/0sTvLCUd/NBXCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:09:09.916500Z"},"content_sha256":"972b85383b4fcd3cf2b10299b5548c0f1cafdfaf264f3fea779d982fafc03997","schema_version":"1.0","event_id":"sha256:972b85383b4fcd3cf2b10299b5548c0f1cafdfaf264f3fea779d982fafc03997"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA/bundle.json","state_url":"https://pith.science/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA/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":"2026-05-26T01:09:09Z","links":{"resolver":"https://pith.science/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA","bundle":"https://pith.science/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA/bundle.json","state":"https://pith.science/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SG7AWKCZNG5YPOXFGUSBGRNXDA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SG7AWKCZNG5YPOXFGUSBGRNXDA","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":"5ca142c66bf6a5754c7447c5f5bf6cd57fc1b190c56f72404c116f7f40b48583","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-29T16:47:14Z","title_canon_sha256":"296d49e8b937aeb405d2fcbd71e3ae91af930753824f2a97d682fd7fb85c3967"},"schema_version":"1.0","source":{"id":"1709.10489","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.10489","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"arxiv_version","alias_value":"1709.10489v3","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.10489","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"pith_short_12","alias_value":"SG7AWKCZNG5Y","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SG7AWKCZNG5YPOXF","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SG7AWKCZ","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:972b85383b4fcd3cf2b10299b5548c0f1cafdfaf264f3fea779d982fafc03997","target":"graph","created_at":"2026-05-18T00:15:42Z","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":"Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these approaches often include a variety of assumptions, are computationally intensive, and do not learn from failures. In contrast, learning-based methods improve as the robot acts in the environment, but are difficult to deploy in the real-world due to their high sample complexity. To address the need to ","authors_text":"Adam Villaflor, Bosen Ding, Gregory Kahn, Pieter Abbeel, Sergey Levine","cross_cats":["cs.AI","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-29T16:47:14Z","title":"Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.10489","kind":"arxiv","version":3},"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":"sha256:3a5c0bc5b722bb9b9ea246682ecc9810f7dc2f984a3cf77303bed141bd94c488","target":"record","created_at":"2026-05-18T00:15:42Z","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":"5ca142c66bf6a5754c7447c5f5bf6cd57fc1b190c56f72404c116f7f40b48583","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-29T16:47:14Z","title_canon_sha256":"296d49e8b937aeb405d2fcbd71e3ae91af930753824f2a97d682fd7fb85c3967"},"schema_version":"1.0","source":{"id":"1709.10489","kind":"arxiv","version":3}},"canonical_sha256":"91be0b285969bb87bae535241345b7180a5a6de5fe122e48ff2523d875f0430c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91be0b285969bb87bae535241345b7180a5a6de5fe122e48ff2523d875f0430c","first_computed_at":"2026-05-18T00:15:42.027706Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:42.027706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ec8nVqPGAhGLBK5ZKsZADlj4mQykNXdtM3hCSjYC4SmdwzzxOIk49IAm2QmRwFtSs7pXBdzV8cX6pWLpXaRzDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:42.028311Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.10489","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a5c0bc5b722bb9b9ea246682ecc9810f7dc2f984a3cf77303bed141bd94c488","sha256:972b85383b4fcd3cf2b10299b5548c0f1cafdfaf264f3fea779d982fafc03997"],"state_sha256":"40f8400f44643affdcc1999ef936b9985e5eacbfff7970f9b715ffc462a9c5a9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q1gPvidTlQVBc/YXfInhh3h9uoWe8bc5OcC/fJ8O4sO8yz7+UG4DGb0YDOyHdBbRrvFKxBUffjWpuRZ/p7J3Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T01:09:09.919589Z","bundle_sha256":"b5889d32a9d56e63caac5f876c30dc419c8f8972ea3016dd38c529d50643eace"}}