{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:66TBS5ZMBPXX7CQINARIR4NR6N","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":"3409372546f2a6ee23cd8f92b73e3295c01e1e9ec497ee02be064fb9464b40fa","cross_cats_sorted":["cs.AI","cs.CV","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-26T23:50:30Z","title_canon_sha256":"054fcb9fbd266b5c5488665a20fb747518d8da82c9f27cea6150ea55c4956f8d"},"schema_version":"1.0","source":{"id":"2010.13957","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.13957","created_at":"2026-07-05T02:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2010.13957v2","created_at":"2026-07-05T02:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.13957","created_at":"2026-07-05T02:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"66TBS5ZMBPXX","created_at":"2026-07-05T02:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"66TBS5ZMBPXX7CQI","created_at":"2026-07-05T02:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"66TBS5ZM","created_at":"2026-07-05T02:05:44Z"}],"graph_snapshots":[{"event_id":"sha256:5bc6cbce657833a63e1053f4e1d06e9a156650e5886b54db7a28e50ae6c9a195","target":"graph","created_at":"2026-07-05T02:05:44Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.13957/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks. However, the onerous data requirements of meta-training compounded with the challenge of learning from sensory inputs such as images have made meta-RL challenging to apply to real robotic systems. Latent state models, which learn compact state representations from a sequence of observations, can accelerate representation learning from visual inputs. In this paper, we leverage the perspective of meta-learning as ","authors_text":"Anusha Nagabandi, Chelsea Finn, Kate Rakelly, Sergey Levine, Tony Z. Zhao","cross_cats":["cs.AI","cs.CV","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-26T23:50:30Z","title":"MELD: Meta-Reinforcement Learning from Images via Latent State Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.13957","kind":"arxiv","version":2},"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":"sha256:8c92d9c5f2c3a0aeac21defac32771ee817146a017975fe6c953d4a6e6e31379","target":"record","created_at":"2026-07-05T02:05:44Z","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":"3409372546f2a6ee23cd8f92b73e3295c01e1e9ec497ee02be064fb9464b40fa","cross_cats_sorted":["cs.AI","cs.CV","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-26T23:50:30Z","title_canon_sha256":"054fcb9fbd266b5c5488665a20fb747518d8da82c9f27cea6150ea55c4956f8d"},"schema_version":"1.0","source":{"id":"2010.13957","kind":"arxiv","version":2}},"canonical_sha256":"f7a619772c0bef7f8a08682288f1b1f3700348d2113fb32f1b76aa76a1ecc820","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7a619772c0bef7f8a08682288f1b1f3700348d2113fb32f1b76aa76a1ecc820","first_computed_at":"2026-07-05T02:05:44.632673Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:05:44.632673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zaouffn9NPGcJTw3QXnYbPdN+rE46+iO2EsDWoh1I66uQwmTG5xu6DQyxMfyaSSMq2bkMbvzyI0BJeCkKHGiCA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:05:44.633059Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.13957","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c92d9c5f2c3a0aeac21defac32771ee817146a017975fe6c953d4a6e6e31379","sha256:5bc6cbce657833a63e1053f4e1d06e9a156650e5886b54db7a28e50ae6c9a195"],"state_sha256":"c26fde601af0955c41a7801f62c9bdff477e6c95c37ce8df24a45c51a712b1b2"}