{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XKQZIPSVO2C5TIHZHLQUR2VR5T","short_pith_number":"pith:XKQZIPSV","schema_version":"1.0","canonical_sha256":"baa1943e557685d9a0f93ae148eab1ecf81073a3b39ebd1114c9e7fc2aa7ded7","source":{"kind":"arxiv","id":"1709.04407","version":2},"attestation_state":"computed","paper":{"title":"An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"cs.RO","authors_text":"Angela P. Schoellig, Mohamed K. Helwa, Siqi Zhou","submitted_at":"2017-09-13T16:28:26Z","abstract_excerpt":"This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedforward approaches are commonly used for improving tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to their inherent instability. In order to resolve the instability issue, existing methods have assumed that the system model is known and used pre-actuation or inverse approximation techniques. In this work, we propose an approach for learning a stable, approxim"},"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":"1709.04407","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-13T16:28:26Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"82b75271226aae37bb6fd2e21331438a47e90e59264e8595f498f91c027cbca0","abstract_canon_sha256":"d2ed7b16243833f7a9fd3c31537b423d0facf6cd6cf15fae7885753cbd20579a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:51.012731Z","signature_b64":"uALm1PQTyc3I7Un2EQtyfnMw+Yp7Y0ISTdZC+uwTuQzrhGcZq839PBa1NAZZemzmFj2bk6ia1p/s4bzRMLrUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"baa1943e557685d9a0f93ae148eab1ecf81073a3b39ebd1114c9e7fc2aa7ded7","last_reissued_at":"2026-05-18T00:21:51.012120Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:51.012120Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"cs.RO","authors_text":"Angela P. Schoellig, Mohamed K. Helwa, Siqi Zhou","submitted_at":"2017-09-13T16:28:26Z","abstract_excerpt":"This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedforward approaches are commonly used for improving tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to their inherent instability. In order to resolve the instability issue, existing methods have assumed that the system model is known and used pre-actuation or inverse approximation techniques. In this work, we propose an approach for learning a stable, approxim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.04407","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.04407","created_at":"2026-05-18T00:21:51.012214+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.04407v2","created_at":"2026-05-18T00:21:51.012214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.04407","created_at":"2026-05-18T00:21:51.012214+00:00"},{"alias_kind":"pith_short_12","alias_value":"XKQZIPSVO2C5","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XKQZIPSVO2C5TIHZ","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XKQZIPSV","created_at":"2026-05-18T12:31:56.362134+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/XKQZIPSVO2C5TIHZHLQUR2VR5T","json":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T.json","graph_json":"https://pith.science/api/pith-number/XKQZIPSVO2C5TIHZHLQUR2VR5T/graph.json","events_json":"https://pith.science/api/pith-number/XKQZIPSVO2C5TIHZHLQUR2VR5T/events.json","paper":"https://pith.science/paper/XKQZIPSV"},"agent_actions":{"view_html":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T","download_json":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T.json","view_paper":"https://pith.science/paper/XKQZIPSV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.04407&json=true","fetch_graph":"https://pith.science/api/pith-number/XKQZIPSVO2C5TIHZHLQUR2VR5T/graph.json","fetch_events":"https://pith.science/api/pith-number/XKQZIPSVO2C5TIHZHLQUR2VR5T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T/action/storage_attestation","attest_author":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T/action/author_attestation","sign_citation":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T/action/citation_signature","submit_replication":"https://pith.science/pith/XKQZIPSVO2C5TIHZHLQUR2VR5T/action/replication_record"}},"created_at":"2026-05-18T00:21:51.012214+00:00","updated_at":"2026-05-18T00:21:51.012214+00:00"}