Cafe-Mpc: A Cascaded-Fidelity Model Predictive Control Framework With Tuning-Free Whole-Body Control
Year: 2024
Type: article
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-Mpcstrategically 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 fromCafe-Mpcis 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 inCafe-Mpcon the tracking performance and required computation time. We also show thatCafe-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, whereCafe-Mpcruns at 50 Hz, and the solver spends on average 5.3 ms per iteration. Results are demonstrated in the accompanying video.
Source: IEEE Transactions on Robotics
Authors: He Li, Patrick M. Wensing
Institution: University of Notre Dame
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Award: G3446393974
OpenAlex ID: W4404576594