{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2CASWATECJ7FOCCAT242QUKDOY","short_pith_number":"pith:2CASWATE","schema_version":"1.0","canonical_sha256":"d0812b0264127e5708409eb9a851437625a0846533ce1a18923ec4f06e410254","source":{"kind":"arxiv","id":"2109.04928","version":4},"attestation_state":"computed","paper":{"title":"Trajectory Optimization with Optimization-Based Dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Pete Florence, Simon Le Cleac'h, Sumeet Singh, Taylor A. Howell, Vikas Sindhwani, Zachary Manchester","submitted_at":"2021-09-10T15:18:09Z","abstract_excerpt":"We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics representation enables constraint handling, additional variables, and non-smooth behavior to be abstracted away from the upper-level optimizer, and allows classical unconstrained optimizers to synthesize trajectories for more complex systems. We provide an interior-point method for efficient evaluation of const"},"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":"2109.04928","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-09-10T15:18:09Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"8ef2b3241fbaf02852814c70d21eab7e5261a3f861ab65fd8eac6893605f45e5","abstract_canon_sha256":"33827b5e219a4df0b4327212cd544b0270d9434f94fb71648cbafcbfef18dd53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:32:20.315331Z","signature_b64":"LZ1d7lB2KMdYd4kJpxZK+0zK/amxoZZ5NK55GfCP6Mz5uxxZ0KcFUdwSCVMnmSbky8KPRJIbRVHo+WZm7nyrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0812b0264127e5708409eb9a851437625a0846533ce1a18923ec4f06e410254","last_reissued_at":"2026-07-05T05:32:20.314766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:32:20.314766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trajectory Optimization with Optimization-Based Dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Pete Florence, Simon Le Cleac'h, Sumeet Singh, Taylor A. Howell, Vikas Sindhwani, Zachary Manchester","submitted_at":"2021-09-10T15:18:09Z","abstract_excerpt":"We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics representation enables constraint handling, additional variables, and non-smooth behavior to be abstracted away from the upper-level optimizer, and allows classical unconstrained optimizers to synthesize trajectories for more complex systems. We provide an interior-point method for efficient evaluation of const"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.04928","kind":"arxiv","version":4},"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/2109.04928/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":"2109.04928","created_at":"2026-07-05T05:32:20.314837+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.04928v4","created_at":"2026-07-05T05:32:20.314837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.04928","created_at":"2026-07-05T05:32:20.314837+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CASWATECJ7F","created_at":"2026-07-05T05:32:20.314837+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CASWATECJ7FOCCA","created_at":"2026-07-05T05:32:20.314837+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CASWATE","created_at":"2026-07-05T05:32:20.314837+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/2CASWATECJ7FOCCAT242QUKDOY","json":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY.json","graph_json":"https://pith.science/api/pith-number/2CASWATECJ7FOCCAT242QUKDOY/graph.json","events_json":"https://pith.science/api/pith-number/2CASWATECJ7FOCCAT242QUKDOY/events.json","paper":"https://pith.science/paper/2CASWATE"},"agent_actions":{"view_html":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY","download_json":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY.json","view_paper":"https://pith.science/paper/2CASWATE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.04928&json=true","fetch_graph":"https://pith.science/api/pith-number/2CASWATECJ7FOCCAT242QUKDOY/graph.json","fetch_events":"https://pith.science/api/pith-number/2CASWATECJ7FOCCAT242QUKDOY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY/action/storage_attestation","attest_author":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY/action/author_attestation","sign_citation":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY/action/citation_signature","submit_replication":"https://pith.science/pith/2CASWATECJ7FOCCAT242QUKDOY/action/replication_record"}},"created_at":"2026-07-05T05:32:20.314837+00:00","updated_at":"2026-07-05T05:32:20.314837+00:00"}