Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking

TL;DR

This paper introduces a multi-phase model predictive control method for bipedal walking that balances detailed and simplified models to optimize joint torques in real-time, validated through simulation.

Contribution

It proposes a novel multi-phase MPC approach combining detailed and simplified models to improve computational efficiency and control accuracy in bipedal walking.

Findings

Successfully validated in MuJoCo simulation on HyPer-2.

Achieved real-time control with reduced computational complexity.

Optimized joint torques without predefining footstep locations.

Abstract

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