{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6TWTCC5SGOZCWRMMDKUGFERLDN","short_pith_number":"pith:6TWTCC5S","schema_version":"1.0","canonical_sha256":"f4ed310bb233b22b458c1aa862922b1b61082c0919a92aa4b50058ea691f25ca","source":{"kind":"arxiv","id":"2310.10606","version":1},"attestation_state":"computed","paper":{"title":"BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Dennis W. Hong, Irfan Essa, K. Niranjan Kumar, Nitish Sontakke, Sehoon Ha, Stefanos Nikolaidis, Tianle Huang","submitted_at":"2023-10-16T17:32:23Z","abstract_excerpt":"Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world. However, DR often requires careful tuning of randomization parameters. Methods like Bayesian Domain Randomization (Bayesian DR) and Active Domain Randomization (Adaptive DR) address this issue by automating parameter range selection using real-world experience. While effective, these algorithms often require long computation time, as a new policy is trained from scratch every iteration. In this work, w"},"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":"2310.10606","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-10-16T17:32:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d5057141214f236912f9c1f8a0836d5a62612c951cc70e4a84aff4e67ad5ff49","abstract_canon_sha256":"fdc58efdc3b7d484185fafb27cc8e7e6cef510f5654b5bb4804e0a28fafed622"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:22.267429Z","signature_b64":"nGUODz9iPl8z6HleHsA0KQx8LX9pPzVqlKQkNdbrlV6LGGt7EbhxB5riYj7kpDwdc3bD+fXaIdjWH24kuQLRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4ed310bb233b22b458c1aa862922b1b61082c0919a92aa4b50058ea691f25ca","last_reissued_at":"2026-07-05T07:01:22.266982Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:22.266982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Dennis W. Hong, Irfan Essa, K. Niranjan Kumar, Nitish Sontakke, Sehoon Ha, Stefanos Nikolaidis, Tianle Huang","submitted_at":"2023-10-16T17:32:23Z","abstract_excerpt":"Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world. However, DR often requires careful tuning of randomization parameters. Methods like Bayesian Domain Randomization (Bayesian DR) and Active Domain Randomization (Adaptive DR) address this issue by automating parameter range selection using real-world experience. While effective, these algorithms often require long computation time, as a new policy is trained from scratch every iteration. In this work, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10606","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/2310.10606/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":"2310.10606","created_at":"2026-07-05T07:01:22.267042+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.10606v1","created_at":"2026-07-05T07:01:22.267042+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10606","created_at":"2026-07-05T07:01:22.267042+00:00"},{"alias_kind":"pith_short_12","alias_value":"6TWTCC5SGOZC","created_at":"2026-07-05T07:01:22.267042+00:00"},{"alias_kind":"pith_short_16","alias_value":"6TWTCC5SGOZCWRMM","created_at":"2026-07-05T07:01:22.267042+00:00"},{"alias_kind":"pith_short_8","alias_value":"6TWTCC5S","created_at":"2026-07-05T07:01:22.267042+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.16475","citing_title":"Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN","json":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN.json","graph_json":"https://pith.science/api/pith-number/6TWTCC5SGOZCWRMMDKUGFERLDN/graph.json","events_json":"https://pith.science/api/pith-number/6TWTCC5SGOZCWRMMDKUGFERLDN/events.json","paper":"https://pith.science/paper/6TWTCC5S"},"agent_actions":{"view_html":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN","download_json":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN.json","view_paper":"https://pith.science/paper/6TWTCC5S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.10606&json=true","fetch_graph":"https://pith.science/api/pith-number/6TWTCC5SGOZCWRMMDKUGFERLDN/graph.json","fetch_events":"https://pith.science/api/pith-number/6TWTCC5SGOZCWRMMDKUGFERLDN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN/action/storage_attestation","attest_author":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN/action/author_attestation","sign_citation":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN/action/citation_signature","submit_replication":"https://pith.science/pith/6TWTCC5SGOZCWRMMDKUGFERLDN/action/replication_record"}},"created_at":"2026-07-05T07:01:22.267042+00:00","updated_at":"2026-07-05T07:01:22.267042+00:00"}