{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GCFLCMUDGBXHBOPAQCOPDI3LCZ","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":"0c919a7ed8b7852f102e92b96f8a67b2014bb1dff37000c4120aea34052edc4c","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-11-11T11:24:55Z","title_canon_sha256":"f1a7be22f554ca5facaf8ed968724b02b9a2ff36b4913292a1780932bba7ee08"},"schema_version":"1.0","source":{"id":"2211.06134","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.06134","created_at":"2026-07-05T06:02:01Z"},{"alias_kind":"arxiv_version","alias_value":"2211.06134v2","created_at":"2026-07-05T06:02:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.06134","created_at":"2026-07-05T06:02:01Z"},{"alias_kind":"pith_short_12","alias_value":"GCFLCMUDGBXH","created_at":"2026-07-05T06:02:01Z"},{"alias_kind":"pith_short_16","alias_value":"GCFLCMUDGBXHBOPA","created_at":"2026-07-05T06:02:01Z"},{"alias_kind":"pith_short_8","alias_value":"GCFLCMUD","created_at":"2026-07-05T06:02:01Z"}],"graph_snapshots":[{"event_id":"sha256:e887f24fb1de5b515c9573f69b3803c9b60472e2586ac4927e5609dd9c3d07ae","target":"graph","created_at":"2026-07-05T06:02:01Z","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/2211.06134/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Solving real-world manipulation tasks requires robots to have a repertoire of skills applicable to a wide range of circumstances. When using learning-based methods to acquire such skills, the key challenge is to obtain training data that covers diverse and feasible variations of the task, which often requires non-trivial manual labor and domain knowledge. In this work, we introduce Active Task Randomization (ATR), an approach that learns robust skills through the unsupervised generation of training tasks. ATR selects suitable tasks, which consist of an initial environment state and manipulatio","authors_text":"Ajay Mandlekar, Jeannette Bohg, Kuan Fang, Li Fei-Fei, Toki Migimatsu","cross_cats":["cs.AI","cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-11-11T11:24:55Z","title":"Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.06134","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":"sha256:838c31525225df7d82568c4c5f654c58dba8ab896dcbbbe60d2d473a000d8e63","target":"record","created_at":"2026-07-05T06:02:01Z","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":"0c919a7ed8b7852f102e92b96f8a67b2014bb1dff37000c4120aea34052edc4c","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-11-11T11:24:55Z","title_canon_sha256":"f1a7be22f554ca5facaf8ed968724b02b9a2ff36b4913292a1780932bba7ee08"},"schema_version":"1.0","source":{"id":"2211.06134","kind":"arxiv","version":2}},"canonical_sha256":"308ab13283306e70b9e0809cf1a36b166e4619ef45111c1171535f3c7417faac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"308ab13283306e70b9e0809cf1a36b166e4619ef45111c1171535f3c7417faac","first_computed_at":"2026-07-05T06:02:01.823464Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:02:01.823464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ch21noHuqMswJkP9OK5RsztplN6B+SPmQhdyWeGSCygtzkiIOPR0FN/oCZEtULlPXnmHFBWKVluewdfCrX67AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:02:01.823871Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.06134","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:838c31525225df7d82568c4c5f654c58dba8ab896dcbbbe60d2d473a000d8e63","sha256:e887f24fb1de5b515c9573f69b3803c9b60472e2586ac4927e5609dd9c3d07ae"],"state_sha256":"c0e27cc84d683c4c32f587a7b780b573ab21f62ec4b08efab1b25027a46653d6"}