A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotion

TL;DR

This paper introduces a modular residual learning framework that enhances model-based locomotion control, improving robustness and efficiency in uncertain environments, validated on a real quadrupedal robot.

Contribution

It proposes a novel modular residual learning approach integrated with model-based control for robust locomotion, demonstrating improved performance and robustness over baseline methods.

Findings

Enhanced control performance in high-uncertainty environments

Higher learning efficiency compared to baseline methods

Successful real-world robot deployment maintaining balance

Abstract

This paper presents a novel approach that combines the advantages of both model-based and learning-based frameworks to achieve robust locomotion. The residual modules are integrated with each corresponding part of the model-based framework, a footstep planner and dynamic model designed using heuristics, to complement performance degradation caused by a model mismatch. By utilizing a modular structure and selecting the appropriate learning-based method for each residual module, our framework demonstrates improved control performance in environments with high uncertainty, while also achieving higher learning efficiency compared to baseline methods. Moreover, we observed that our proposed methodology not only enhances control performance but also provides additional benefits, such as making nominal controllers more robust to parameter tuning. To investigate the feasibility of our framework, we demonstrated residual modules combined with model predictive control in a real quadrupedal robot. Despite uncertainties beyond the simulation, the robot successfully maintains balance and tracks the commanded velocity.