{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:XFYXILXM6KSSNXTOHASKTVBQH4","short_pith_number":"pith:XFYXILXM","schema_version":"1.0","canonical_sha256":"b971742eecf2a526de6e3824a9d4303f3301ed0b704f71ef24f2b49d81f730d3","source":{"kind":"arxiv","id":"2212.02941","version":3},"attestation_state":"computed","paper":{"title":"Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.RO","authors_text":"Jan Swevers, Joschka Boedecker, Moritz Diehl, Ruan Viljoen, Rudolf Reiter, Seyed Mahdi Basiri Azad, Shamil Mamedov","submitted_at":"2022-12-06T12:54:08Z","abstract_excerpt":"Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. Nonlinear model predictive control (NMPC) offers an effective means to control such robots, but its significant computational demand often limits its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe appro"},"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":"2212.02941","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-12-06T12:54:08Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"cb14f3c0e4820d2d98b0908b899105d3c6efb1f7d9fa4f1092f8f715590d9688","abstract_canon_sha256":"77e24ede177a9ce293e17a332a910a553610ae3b89898ab4d5ab9997b7cef8e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:55:30.261446Z","signature_b64":"mui9jXrorOYCcJg6siQgEgUBB6Gfgg0nwTzYVv6dFtcYeUuQFWUjDcm0LKI/KkuR5/tacAzIXIZuDPYUYOCBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b971742eecf2a526de6e3824a9d4303f3301ed0b704f71ef24f2b49d81f730d3","last_reissued_at":"2026-07-05T08:55:30.260941Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:55:30.260941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.RO","authors_text":"Jan Swevers, Joschka Boedecker, Moritz Diehl, Ruan Viljoen, Rudolf Reiter, Seyed Mahdi Basiri Azad, Shamil Mamedov","submitted_at":"2022-12-06T12:54:08Z","abstract_excerpt":"Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. Nonlinear model predictive control (NMPC) offers an effective means to control such robots, but its significant computational demand often limits its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe appro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.02941","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/2212.02941/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":"2212.02941","created_at":"2026-07-05T08:55:30.261001+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.02941v3","created_at":"2026-07-05T08:55:30.261001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.02941","created_at":"2026-07-05T08:55:30.261001+00:00"},{"alias_kind":"pith_short_12","alias_value":"XFYXILXM6KSS","created_at":"2026-07-05T08:55:30.261001+00:00"},{"alias_kind":"pith_short_16","alias_value":"XFYXILXM6KSSNXTO","created_at":"2026-07-05T08:55:30.261001+00:00"},{"alias_kind":"pith_short_8","alias_value":"XFYXILXM","created_at":"2026-07-05T08:55:30.261001+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/XFYXILXM6KSSNXTOHASKTVBQH4","json":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4.json","graph_json":"https://pith.science/api/pith-number/XFYXILXM6KSSNXTOHASKTVBQH4/graph.json","events_json":"https://pith.science/api/pith-number/XFYXILXM6KSSNXTOHASKTVBQH4/events.json","paper":"https://pith.science/paper/XFYXILXM"},"agent_actions":{"view_html":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4","download_json":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4.json","view_paper":"https://pith.science/paper/XFYXILXM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.02941&json=true","fetch_graph":"https://pith.science/api/pith-number/XFYXILXM6KSSNXTOHASKTVBQH4/graph.json","fetch_events":"https://pith.science/api/pith-number/XFYXILXM6KSSNXTOHASKTVBQH4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4/action/storage_attestation","attest_author":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4/action/author_attestation","sign_citation":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4/action/citation_signature","submit_replication":"https://pith.science/pith/XFYXILXM6KSSNXTOHASKTVBQH4/action/replication_record"}},"created_at":"2026-07-05T08:55:30.261001+00:00","updated_at":"2026-07-05T08:55:30.261001+00:00"}