{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:I2MC7ZHTIA6PWFXVZOSR3T2QER","short_pith_number":"pith:I2MC7ZHT","schema_version":"1.0","canonical_sha256":"46982fe4f3403cfb16f5cba51dcf50246e881c90f422d9a8f69d1fa8ce048488","source":{"kind":"arxiv","id":"2201.09562","version":5},"attestation_state":"computed","paper":{"title":"GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Andreas Krause, Bhavya Sukhija, David Lindner, Dominik Baumann, Matteo Turchetta, Sebastian Trimpe","submitted_at":"2022-01-24T10:05:44Z","abstract_excerpt":"Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for high-dimensional systems while giving safety an"},"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":"2201.09562","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-01-24T10:05:44Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"80e447c847cf3ed89ea3355d47ab543dc9cb2ff502d6bf115006b3899ea3ffb5","abstract_canon_sha256":"3367a9711207f784fcf54aceb88b13a60f12fd841ab46e36d8aa913bcd7d9a7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:19:33.839130Z","signature_b64":"L7anXKYcRhMK846Th0cCZ5AtGHLKs1pCYyqu7GHhfnTSztF1xOxSMI1Y8qIF7/irbunE79Vnhy9Zu77vvbofDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46982fe4f3403cfb16f5cba51dcf50246e881c90f422d9a8f69d1fa8ce048488","last_reissued_at":"2026-07-05T06:19:33.838589Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:19:33.838589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Andreas Krause, Bhavya Sukhija, David Lindner, Dominik Baumann, Matteo Turchetta, Sebastian Trimpe","submitted_at":"2022-01-24T10:05:44Z","abstract_excerpt":"Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for high-dimensional systems while giving safety an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.09562","kind":"arxiv","version":5},"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/2201.09562/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":"2201.09562","created_at":"2026-07-05T06:19:33.838647+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.09562v5","created_at":"2026-07-05T06:19:33.838647+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.09562","created_at":"2026-07-05T06:19:33.838647+00:00"},{"alias_kind":"pith_short_12","alias_value":"I2MC7ZHTIA6P","created_at":"2026-07-05T06:19:33.838647+00:00"},{"alias_kind":"pith_short_16","alias_value":"I2MC7ZHTIA6PWFXV","created_at":"2026-07-05T06:19:33.838647+00:00"},{"alias_kind":"pith_short_8","alias_value":"I2MC7ZHT","created_at":"2026-07-05T06:19:33.838647+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER","json":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER.json","graph_json":"https://pith.science/api/pith-number/I2MC7ZHTIA6PWFXVZOSR3T2QER/graph.json","events_json":"https://pith.science/api/pith-number/I2MC7ZHTIA6PWFXVZOSR3T2QER/events.json","paper":"https://pith.science/paper/I2MC7ZHT"},"agent_actions":{"view_html":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER","download_json":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER.json","view_paper":"https://pith.science/paper/I2MC7ZHT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.09562&json=true","fetch_graph":"https://pith.science/api/pith-number/I2MC7ZHTIA6PWFXVZOSR3T2QER/graph.json","fetch_events":"https://pith.science/api/pith-number/I2MC7ZHTIA6PWFXVZOSR3T2QER/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER/action/storage_attestation","attest_author":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER/action/author_attestation","sign_citation":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER/action/citation_signature","submit_replication":"https://pith.science/pith/I2MC7ZHTIA6PWFXVZOSR3T2QER/action/replication_record"}},"created_at":"2026-07-05T06:19:33.838647+00:00","updated_at":"2026-07-05T06:19:33.838647+00:00"}