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This paper introduces a cascaded-fidelity Model Predictive Control (MPC) for bipedal walking, leveraging a detailed whole-body model for short-term predictions and a simplified single-rigid-body model for longer horizons. This approach reduces computational cost while maintaining predictive accuracy by optimizing joint torques based on a predefined contact schedule and target walking speed, without pre-selecting footstep locations. The controller is implemented using SQP in acados and validated on the 18-DoF HyPer-2 robot in MuJoCo.
Achieve real-time bipedal walking control by cleverly swapping high-fidelity for low-fidelity models in MPC, slashing computation without sacrificing stability.
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. The resulting nonlinear optimal control problem is solved using sequential quadratic programming (SQP) in acados. Using a prior specified contact schedule and a target walking speed, the controller optimizes joint torques without depending on prior selected foot step locations. The controller is validated in MuJoCo simulation on the 18-DoF bipedal robot HyPer-2
