{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:52JSEDNHNYU2EI6VBHF74XCO3U","short_pith_number":"pith:52JSEDNH","canonical_record":{"source":{"id":"1703.09260","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-03-27T18:38:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ae9aa2a8087a78cb44574f051dd566e5a35060134b090ddbef0e2561efa255af","abstract_canon_sha256":"cdbb733aeac43575cb65848ba160c25f97053bca891b3f2341bf58a62582accb"},"schema_version":"1.0"},"canonical_sha256":"ee93220da76e29a223d509cbfe5c4edd3c110d4e49ed667e2bf1171186218e7c","source":{"kind":"arxiv","id":"1703.09260","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.09260","created_at":"2026-05-18T00:34:35Z"},{"alias_kind":"arxiv_version","alias_value":"1703.09260v2","created_at":"2026-05-18T00:34:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09260","created_at":"2026-05-18T00:34:35Z"},{"alias_kind":"pith_short_12","alias_value":"52JSEDNHNYU2","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"52JSEDNHNYU2EI6V","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"52JSEDNH","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:52JSEDNHNYU2EI6VBHF74XCO3U","target":"record","payload":{"canonical_record":{"source":{"id":"1703.09260","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-03-27T18:38:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ae9aa2a8087a78cb44574f051dd566e5a35060134b090ddbef0e2561efa255af","abstract_canon_sha256":"cdbb733aeac43575cb65848ba160c25f97053bca891b3f2341bf58a62582accb"},"schema_version":"1.0"},"canonical_sha256":"ee93220da76e29a223d509cbfe5c4edd3c110d4e49ed667e2bf1171186218e7c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:35.045882Z","signature_b64":"WagdbD2fp4TW7U1qrzpSuLhrl5UFRODcfvmTHSHhQPMgewIXMA+ZIen62qEGBNQ1ubUdYxSK6Xb0OszTL+HADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee93220da76e29a223d509cbfe5c4edd3c110d4e49ed667e2bf1171186218e7c","last_reissued_at":"2026-05-18T00:34:35.045476Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:35.045476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.09260","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:34:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PBWy8/orwoNGTZEXvSvk2xYmTDcWblBabgdN7805pbxGKptRACkHj/v947DFAGw1o2Xck59kNzfSZyc9XHPeBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T15:53:29.545370Z"},"content_sha256":"1027427aabc470d643b3d6c28ea3e76e352435eba8e69582ee775cf58766883b","schema_version":"1.0","event_id":"sha256:1027427aabc470d643b3d6c28ea3e76e352435eba8e69582ee775cf58766883b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:52JSEDNHNYU2EI6VBHF74XCO3U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Goal-Driven Dynamics Learning via Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SY","authors_text":"Claire J. Tomlin, Roberto Calandra, Sergey Levine, Somil Bansal, Ted Xiao","submitted_at":"2017-03-27T18:38:06Z","abstract_excerpt":"Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the focus is on explicitly learning the dynamics model which achieves the best control performance for the task at hand, rather than learning the true dynamics. In this work, we use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing the control performance, and used "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09260","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:34:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/fDQXiRn3w7UeVZiQpFRtq572reloqvsa0i1GrvctbFfJKjD67DH1RuDE4OpBoGSeXQ4R8BrHd8fEpMjvDu3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T15:53:29.546057Z"},"content_sha256":"23fc4cd5ba62971e81f235b78c9b8196415dd9b4fcaf509290aa75156a56d2cd","schema_version":"1.0","event_id":"sha256:23fc4cd5ba62971e81f235b78c9b8196415dd9b4fcaf509290aa75156a56d2cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/52JSEDNHNYU2EI6VBHF74XCO3U/bundle.json","state_url":"https://pith.science/pith/52JSEDNHNYU2EI6VBHF74XCO3U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/52JSEDNHNYU2EI6VBHF74XCO3U/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T15:53:29Z","links":{"resolver":"https://pith.science/pith/52JSEDNHNYU2EI6VBHF74XCO3U","bundle":"https://pith.science/pith/52JSEDNHNYU2EI6VBHF74XCO3U/bundle.json","state":"https://pith.science/pith/52JSEDNHNYU2EI6VBHF74XCO3U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/52JSEDNHNYU2EI6VBHF74XCO3U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:52JSEDNHNYU2EI6VBHF74XCO3U","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":"cdbb733aeac43575cb65848ba160c25f97053bca891b3f2341bf58a62582accb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-03-27T18:38:06Z","title_canon_sha256":"ae9aa2a8087a78cb44574f051dd566e5a35060134b090ddbef0e2561efa255af"},"schema_version":"1.0","source":{"id":"1703.09260","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.09260","created_at":"2026-05-18T00:34:35Z"},{"alias_kind":"arxiv_version","alias_value":"1703.09260v2","created_at":"2026-05-18T00:34:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09260","created_at":"2026-05-18T00:34:35Z"},{"alias_kind":"pith_short_12","alias_value":"52JSEDNHNYU2","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"52JSEDNHNYU2EI6V","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"52JSEDNH","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:23fc4cd5ba62971e81f235b78c9b8196415dd9b4fcaf509290aa75156a56d2cd","target":"graph","created_at":"2026-05-18T00:34:35Z","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"},"paper":{"abstract_excerpt":"Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the focus is on explicitly learning the dynamics model which achieves the best control performance for the task at hand, rather than learning the true dynamics. In this work, we use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing the control performance, and used ","authors_text":"Claire J. Tomlin, Roberto Calandra, Sergey Levine, Somil Bansal, Ted Xiao","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-03-27T18:38:06Z","title":"Goal-Driven Dynamics Learning via Bayesian Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09260","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":"sha256:1027427aabc470d643b3d6c28ea3e76e352435eba8e69582ee775cf58766883b","target":"record","created_at":"2026-05-18T00:34:35Z","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":"cdbb733aeac43575cb65848ba160c25f97053bca891b3f2341bf58a62582accb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-03-27T18:38:06Z","title_canon_sha256":"ae9aa2a8087a78cb44574f051dd566e5a35060134b090ddbef0e2561efa255af"},"schema_version":"1.0","source":{"id":"1703.09260","kind":"arxiv","version":2}},"canonical_sha256":"ee93220da76e29a223d509cbfe5c4edd3c110d4e49ed667e2bf1171186218e7c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee93220da76e29a223d509cbfe5c4edd3c110d4e49ed667e2bf1171186218e7c","first_computed_at":"2026-05-18T00:34:35.045476Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:35.045476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WagdbD2fp4TW7U1qrzpSuLhrl5UFRODcfvmTHSHhQPMgewIXMA+ZIen62qEGBNQ1ubUdYxSK6Xb0OszTL+HADA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:35.045882Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.09260","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1027427aabc470d643b3d6c28ea3e76e352435eba8e69582ee775cf58766883b","sha256:23fc4cd5ba62971e81f235b78c9b8196415dd9b4fcaf509290aa75156a56d2cd"],"state_sha256":"3fb3faa8f8459243ea70b1208d9e256e0f919307927f983b9b7ca3e7a5a9bbe3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mgq2ywHBcXHl25cKbRjBcULxsftSHsVKDY61s89bf6fohn24VKatFfAgXV2Dz9rI9mhd4juk1ll7ypQkVwaLAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T15:53:29.550057Z","bundle_sha256":"e4f73b625ca59baf1b7eeed634c6da7b0737c6ad9ac8ae0e4dd473a505ae2722"}}