{"paper":{"title":"Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A multi-phase MPC uses a full body model only near term and a simpler rigid-body model farther ahead to control bipedal walking in real time.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dennis Mronga, Felix Wiebe, Franek Stark, Frank Kirchner, Shubham Vyas","submitted_at":"2026-05-06T07:54:49Z","abstract_excerpt":"This paper presents a multi-phase whole-body model predictive control (MPC) approach for bipedal walking, combining a detailed whole-body model in the near horizon with a simplified single-rigid-body model in the later prediction steps. This reduces computational complexity while retaining prediction capabilities. The resulting nonlinear optimal control problem is solved entirely within the general-purpose, off-the-shelf nonlinear MPC framework acados, using sequential quadratic programming (SQP). Given a contact schedule and a target walking speed, the controller optimizes joint torques witho"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This paper presents a multi-phase whole-body model predictive control approach for bipedal walking, combining a detailed whole-body model in the near horizon with a simplified single-rigid-body model in the later prediction steps. This reduces computational complexity while retaining prediction capabilities.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The simplified single-rigid-body model remains sufficiently accurate over the longer prediction horizon that the overall closed-loop walking behavior stays stable and the torque commands remain feasible when transferred to the real robot.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A real-time MPC for bipedal robots uses a detailed whole-body model near-term and a simplified rigid-body model later, solved with SQP in acados and tested in MuJoCo simulation on the HyPer-2 robot.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A multi-phase MPC uses a full body model only near term and a simpler rigid-body model farther ahead to control bipedal walking in real time.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a279c7178e3ef7cb3326f9b15e46ca22a4405b33404e1d47a05f94a4422655e8"},"source":{"id":"2605.04607","kind":"arxiv","version":2},"verdict":{"id":"c8903468-3222-40e5-b0e8-f33cd67ff0a0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T16:12:15.626403Z","strongest_claim":"This paper presents a multi-phase whole-body model predictive control approach for bipedal walking, combining a detailed whole-body model in the near horizon with a simplified single-rigid-body model in the later prediction steps. This reduces computational complexity while retaining prediction capabilities.","one_line_summary":"A real-time MPC for bipedal robots uses a detailed whole-body model near-term and a simplified rigid-body model later, solved with SQP in acados and tested in MuJoCo simulation on the HyPer-2 robot.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The simplified single-rigid-body model remains sufficiently accurate over the longer prediction horizon that the overall closed-loop walking behavior stays stable and the torque commands remain feasible when transferred to the real robot.","pith_extraction_headline":"A multi-phase MPC uses a full body model only near term and a simpler rigid-body model farther ahead to control bipedal walking in real time."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.04607/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T11:37:05.542659Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T22:31:20.006118Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:18:44.919406Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"052865f492806ac2c937341da07599696c708754e12e6660be73d06cc95cded4"},"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"}