{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:4YJKMNJXIF7UJFS5USSHY36FEV","short_pith_number":"pith:4YJKMNJX","canonical_record":{"source":{"id":"2204.04558","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2022-04-09T22:07:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f3b8a9646d117a2328d2afd007eb72b1cae8b8d140c6768597cfe4c0f0dcad19","abstract_canon_sha256":"6752c6853f52c9420696401567aa8951121cc031692e9cfa59251adb54f36375"},"schema_version":"1.0"},"canonical_sha256":"e612a63537417f44965da4a47c6fc525670648b470f857b031c29130df1d2d29","source":{"kind":"arxiv","id":"2204.04558","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.04558","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2204.04558v3","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.04558","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"4YJKMNJXIF7U","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"4YJKMNJXIF7UJFS5","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"4YJKMNJX","created_at":"2026-07-05T06:24:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:4YJKMNJXIF7UJFS5USSHY36FEV","target":"record","payload":{"canonical_record":{"source":{"id":"2204.04558","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2022-04-09T22:07:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f3b8a9646d117a2328d2afd007eb72b1cae8b8d140c6768597cfe4c0f0dcad19","abstract_canon_sha256":"6752c6853f52c9420696401567aa8951121cc031692e9cfa59251adb54f36375"},"schema_version":"1.0"},"canonical_sha256":"e612a63537417f44965da4a47c6fc525670648b470f857b031c29130df1d2d29","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:23.614062Z","signature_b64":"hK5SJ5m8Bec8y9qm8jammSoCzUmG5sJMOoW8aGsntfBYXp2UwPSQYVOPmWsh2A4sFLLGEqN/1kBuucu6DQiPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e612a63537417f44965da4a47c6fc525670648b470f857b031c29130df1d2d29","last_reissued_at":"2026-07-05T06:24:23.613599Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:23.613599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.04558","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p/1+y/3rRBEKZeOcvMK6X1EsM/Echz+ppR6tKwFNPwYv2dGtIaLV7/TWB3L5VkPp6Ga/MH+BP6EkuFmoGRyeCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:05:04.730799Z"},"content_sha256":"bb25c3bd4a93ea1c7a064429a72341b03e62418a3dafa367ec55d95508180800","schema_version":"1.0","event_id":"sha256:bb25c3bd4a93ea1c7a064429a72341b03e62418a3dafa367ec55d95508180800"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:4YJKMNJXIF7UJFS5USSHY36FEV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gradient-Based Trajectory Optimization With Learned Dynamics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Andreas Krause, Bhavya Sukhija, Miguel Zamora, Nathanael K\\\"ohler, Sebastian Curi, Simon Zimmermann, Stelian Coros","submitted_at":"2022-04-09T22:07:34Z","abstract_excerpt":"Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can only be captured to a limited extent. An alternative approach is to leverage machine learning techniques to learn a differentiable dynamics model of the system from data. In this work, we use trajectory optimization and model learning for performing highly dynamic and complex tasks with robotic systems in absence of accurate analytical models of the dynamics. W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.04558","kind":"arxiv","version":3},"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/2204.04558/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"66YmyLeLCmi44s8VYsEjvisVqY9obbiuMJBIJniyg2iLbGEAZIFvB1MDNM6IxqOon/qzbfSOFMi8mOA5e+66DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:05:04.731185Z"},"content_sha256":"4c2bac45c7ac7e9b225df1e4b6e794041535a4720ac46d1f3dac0896154faeb3","schema_version":"1.0","event_id":"sha256:4c2bac45c7ac7e9b225df1e4b6e794041535a4720ac46d1f3dac0896154faeb3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4YJKMNJXIF7UJFS5USSHY36FEV/bundle.json","state_url":"https://pith.science/pith/4YJKMNJXIF7UJFS5USSHY36FEV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4YJKMNJXIF7UJFS5USSHY36FEV/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":"2026-07-06T19:05:04Z","links":{"resolver":"https://pith.science/pith/4YJKMNJXIF7UJFS5USSHY36FEV","bundle":"https://pith.science/pith/4YJKMNJXIF7UJFS5USSHY36FEV/bundle.json","state":"https://pith.science/pith/4YJKMNJXIF7UJFS5USSHY36FEV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4YJKMNJXIF7UJFS5USSHY36FEV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:4YJKMNJXIF7UJFS5USSHY36FEV","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":"6752c6853f52c9420696401567aa8951121cc031692e9cfa59251adb54f36375","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2022-04-09T22:07:34Z","title_canon_sha256":"f3b8a9646d117a2328d2afd007eb72b1cae8b8d140c6768597cfe4c0f0dcad19"},"schema_version":"1.0","source":{"id":"2204.04558","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.04558","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2204.04558v3","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.04558","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"4YJKMNJXIF7U","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"4YJKMNJXIF7UJFS5","created_at":"2026-07-05T06:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"4YJKMNJX","created_at":"2026-07-05T06:24:23Z"}],"graph_snapshots":[{"event_id":"sha256:4c2bac45c7ac7e9b225df1e4b6e794041535a4720ac46d1f3dac0896154faeb3","target":"graph","created_at":"2026-07-05T06:24:23Z","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/2204.04558/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can only be captured to a limited extent. An alternative approach is to leverage machine learning techniques to learn a differentiable dynamics model of the system from data. In this work, we use trajectory optimization and model learning for performing highly dynamic and complex tasks with robotic systems in absence of accurate analytical models of the dynamics. W","authors_text":"Andreas Krause, Bhavya Sukhija, Miguel Zamora, Nathanael K\\\"ohler, Sebastian Curi, Simon Zimmermann, Stelian Coros","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2022-04-09T22:07:34Z","title":"Gradient-Based Trajectory Optimization With Learned Dynamics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.04558","kind":"arxiv","version":3},"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:bb25c3bd4a93ea1c7a064429a72341b03e62418a3dafa367ec55d95508180800","target":"record","created_at":"2026-07-05T06:24:23Z","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":"6752c6853f52c9420696401567aa8951121cc031692e9cfa59251adb54f36375","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2022-04-09T22:07:34Z","title_canon_sha256":"f3b8a9646d117a2328d2afd007eb72b1cae8b8d140c6768597cfe4c0f0dcad19"},"schema_version":"1.0","source":{"id":"2204.04558","kind":"arxiv","version":3}},"canonical_sha256":"e612a63537417f44965da4a47c6fc525670648b470f857b031c29130df1d2d29","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e612a63537417f44965da4a47c6fc525670648b470f857b031c29130df1d2d29","first_computed_at":"2026-07-05T06:24:23.613599Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:24:23.613599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hK5SJ5m8Bec8y9qm8jammSoCzUmG5sJMOoW8aGsntfBYXp2UwPSQYVOPmWsh2A4sFLLGEqN/1kBuucu6DQiPAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:24:23.614062Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.04558","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb25c3bd4a93ea1c7a064429a72341b03e62418a3dafa367ec55d95508180800","sha256:4c2bac45c7ac7e9b225df1e4b6e794041535a4720ac46d1f3dac0896154faeb3"],"state_sha256":"1f709434c74335ab7e3576a007eba98062bac44a107dc7d5d264c929128e3107"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eqPZ90KLGsY4g64C3YcgunDaB9CdreboMAZfifS786aKp8l92yPoKc07/1tPYd1FvUwynsd1ZqdbOSsU6QsnBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:05:04.735501Z","bundle_sha256":"33c567023dee84a2d8e45cbfe72f501ea0233c72edc7ee07a5426eab5b0b5cbb"}}