Cafe-Mpc: A Cascaded-Fidelity Model Predictive Control Framework With Tuning-Free Whole-Body Control

Abstract

This work introduces an optimization-based planning and control framework for real-time synthesis of whole-body motions for legged robots. At the core of the proposed framework is a cascaded-fidelity model predictive controller ( Cafe-Mpc ). Cafe-Mpc strategically relaxes the planning problem along the prediction horizon (i.e., with descending model fidelity, increasingly coarse time steps, and relaxed constraints) for computational and performance gains. This problem is numerically solved with an efficient customized multiple-shooting iLQR solver that is tailored for hybrid systems. The action-value function from Cafe-Mpc is then used as the basis for a new value-function-based whole-body control (VWBC) technique that avoids additional tuning. In this respect, the proposed framework unifies whole-body MPC and more conventional whole-body quadratic programming, which have been treated as separate components in previous works. We study the effects of the cascaded relaxations in Cafe-Mpc on the tracking performance and required computation time. We also show that Cafe-Mpc , if configured appropriately, advances the performance of whole-body MPC without necessarily increasing computational cost. Furthermore, we show the superior performance of VWBC over a conventional Riccati feedback controller in terms of constraint handling. The proposed framework enables accomplishing a gymnastic-style running barrel roll for the first time on quadruped hardware, where Cafe-Mpc runs at 50 Hz, and the solver spends on average 5.3 ms per iteration. Results are demonstrated in the accompanying video.