Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking
Franek Stark, Felix Wiebe, Shubham Vyas, Dennis Mronga, Frank Kirchner
In ICRA Workshop on Frontiers of Optimization for Robotics, 2nd Edition, 1.6.2026, Vienna, IEEE, 2026.
Zusammenfassung (Abstract)
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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 without depending on preselected footstep locations. The controller is validated in MuJoCo simulation on the 18-DoF bipedal robot HyPer-2.
Stichworte
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Model Predictive Control, Bipedal Walking, Solvers, Nonlinear Optimization
Files:
paper_final.pdf
Links:
https://openreview.net/forum?id=UiA6XLlXVS