Benchmarking Model Predictive Control and Reinforcement Learning Based Control for Legged Robot Locomotion in MuJoCo Simulation

TL;DR

This paper compares Model Predictive Control and Reinforcement Learning for legged robot locomotion in simulation, highlighting their respective strengths and limitations in disturbance handling, energy efficiency, and terrain adaptability.

Contribution

It provides a standardized benchmark of MPC and RL controllers on a quadruped robot, offering insights into their performance differences under identical conditions.

Findings

RL handles disturbances and energy efficiency well

MPC recovers better from large perturbations

RL struggles with terrain generalization

Abstract

Model Predictive Control (MPC) and Reinforcement Learning (RL) are two prominent strategies for controlling legged robots, each with unique strengths. RL learns control policies through system interaction, adapting to various scenarios, whereas MPC relies on a predefined mathematical model to solve optimization problems in real-time. Despite their widespread use, there is a lack of direct comparative analysis under standardized conditions. This work addresses this gap by benchmarking MPC and RL controllers on a Unitree Go1 quadruped robot within the MuJoCo simulation environment, focusing on a standardized task-straight walking at a constant velocity. Performance is evaluated based on disturbance rejection, energy efficiency, and terrain adaptability. The results show that RL excels in handling disturbances and maintaining energy efficiency but struggles with generalization to new terrains due to its dependence on learned policies tailored to specific environments. In contrast, MPC shows enhanced recovery capabilities from larger perturbations by leveraging its optimization-based approach, allowing for a balanced distribution of control efforts across the robot's joints. The results provide a clear understanding of the advantages and limitations of both RL and MPC, offering insights into selecting an appropriate control strategy for legged robotic applications.