Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control

Carnegie Mellon University
under review
Teaser Image

A Unitree Go1 robot pushing a box to a desired location with MPPI on hardware (top row) and corresponding MuJoCo simulation states (bottom row) on a single sequence. Contact-rich behaviors like body pushes and leg kicks emerge in real-time without manual pre-specification or offline policy training.

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 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.

Video Presentation

BibTeX


      @article{alvarez2024realtime,
        title={Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control},
        author={Alvarez-Padilla, Juan and Zhang, John Z. and Kwok, Sofia and Dolan, John M. and Manchester, Zachary},
        year={2024},
        journal={arXiv preprint arXiv:2409.10469},
        note={Available at: \url{https://arxiv.org/abs/2409.10469}}
      }