Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control
Juan Alvarez-Padilla, John Z. Zhang, Sofia Kwok, John M. Dolan, Zachary Manchester
Abstract
This paper presents a system for enabling real-time synthesis of whole-body locomotion and manipulation policies for real-world legged robots. Motivated by recent advancements in robot simulation, we leverage the efficient parallelization capabilities of the MuJoCo simulator on a multi-core CPU to achieve fast sampling over the robot state and action trajectories. Our results show surprisingly effective real-world locomotion and manipulation capabilities with a very simple control strategy. We demonstrate our approach on several hardware and simulation experiments: robust locomotion over flat and uneven terrains, climbing over a box whose height is comparable to the robot, and pushing a box to a goal position. To our knowledge, this is the first successful deployment of whole-body sampling-based MPC on real-world legged robot hardware. Experiment videos and code can be found at: whole-body-mppi.github.io.
BibTeX
@inproceedings{icra2025_realtimewholebod,
title = {Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control},
author = {Juan Alvarez-Padilla and John Z. Zhang and Sofia Kwok and John M. Dolan and Zachary Manchester},
booktitle = {ICRA 2025},
year = {2025}
}