{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AE7DEZIOIYLCU5CN67PLWTOO6S","short_pith_number":"pith:AE7DEZIO","schema_version":"1.0","canonical_sha256":"013e32650e46162a744df7debb4dcef48cc85600c68ed0a0eae4829e9582c783","source":{"kind":"arxiv","id":"2305.12240","version":2},"attestation_state":"computed","paper":{"title":"Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Beomsu Kim, Jungwi Mun, Junwon Seo, Seongil Hong, Taekyung Kim","submitted_at":"2023-05-20T17:20:12Z","abstract_excerpt":"In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computational capabilities, this approach is becoming increasingly important for solving challenging control problems in robotics by learning unknown or partially known robot dynamics. Active exploration, in which a robot directs itself to states that yield the highest information gain, is essential for efficient data collection and minimizing human supervision. Similarly, uncertainty"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.12240","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-05-20T17:20:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7827815992724392e80cb96f32fa8c173be65f5162455968f5e197fae12091ef","abstract_canon_sha256":"3625ddca8e02f094215c1114ff1447237937b4f00311635f98d93f309c4e82e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:42.973470Z","signature_b64":"7G3Er6x3M+1uR/k5UhFayjEdXklWWWuB1JTnQ9L1p/BacnGPUgbEJs0PydHLh9hVJwtjMXvuYagMOa1QNGgaCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"013e32650e46162a744df7debb4dcef48cc85600c68ed0a0eae4829e9582c783","last_reissued_at":"2026-07-05T06:14:42.973116Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:42.973116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Beomsu Kim, Jungwi Mun, Junwon Seo, Seongil Hong, Taekyung Kim","submitted_at":"2023-05-20T17:20:12Z","abstract_excerpt":"In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computational capabilities, this approach is becoming increasingly important for solving challenging control problems in robotics by learning unknown or partially known robot dynamics. Active exploration, in which a robot directs itself to states that yield the highest information gain, is essential for efficient data collection and minimizing human supervision. Similarly, uncertainty"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12240","kind":"arxiv","version":2},"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/2305.12240/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.12240","created_at":"2026-07-05T06:14:42.973170+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12240v2","created_at":"2026-07-05T06:14:42.973170+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12240","created_at":"2026-07-05T06:14:42.973170+00:00"},{"alias_kind":"pith_short_12","alias_value":"AE7DEZIOIYLC","created_at":"2026-07-05T06:14:42.973170+00:00"},{"alias_kind":"pith_short_16","alias_value":"AE7DEZIOIYLCU5CN","created_at":"2026-07-05T06:14:42.973170+00:00"},{"alias_kind":"pith_short_8","alias_value":"AE7DEZIO","created_at":"2026-07-05T06:14:42.973170+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12628","citing_title":"Multistep Belief Space Dynamics Learning For Risk-Aware Control","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S","json":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S.json","graph_json":"https://pith.science/api/pith-number/AE7DEZIOIYLCU5CN67PLWTOO6S/graph.json","events_json":"https://pith.science/api/pith-number/AE7DEZIOIYLCU5CN67PLWTOO6S/events.json","paper":"https://pith.science/paper/AE7DEZIO"},"agent_actions":{"view_html":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S","download_json":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S.json","view_paper":"https://pith.science/paper/AE7DEZIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12240&json=true","fetch_graph":"https://pith.science/api/pith-number/AE7DEZIOIYLCU5CN67PLWTOO6S/graph.json","fetch_events":"https://pith.science/api/pith-number/AE7DEZIOIYLCU5CN67PLWTOO6S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S/action/storage_attestation","attest_author":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S/action/author_attestation","sign_citation":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S/action/citation_signature","submit_replication":"https://pith.science/pith/AE7DEZIOIYLCU5CN67PLWTOO6S/action/replication_record"}},"created_at":"2026-07-05T06:14:42.973170+00:00","updated_at":"2026-07-05T06:14:42.973170+00:00"}