Hybrid LMC: Hybrid Learning and Model-based Control for Wheeled Humanoid Robot via Ensemble Deep Reinforcement Learning

TL;DR

This paper introduces Hybrid LMC, a combined learning and model-based control approach using ensemble deep reinforcement learning and classical LQR to improve wheeled humanoid robot locomotion stability and efficiency.

Contribution

It presents a novel hybrid control framework that integrates ensemble deep RL with classical LQR for improved stability and sample efficiency in robot locomotion.

Findings

Hybrid LMC outperforms existing techniques in simulation.

The approach demonstrates increased sample efficiency.

Hybrid LMC maintains stable performance during early training stages.

Abstract

Control of wheeled humanoid locomotion is a challenging problem due to the nonlinear dynamics and under-actuated characteristics of these robots. Traditionally, feedback controllers have been utilized for stabilization and locomotion. However, these methods are often limited by the fidelity of the underlying model used, choice of controller, and environmental variables considered (surface type, ground inclination, etc). Recent advances in reinforcement learning (RL) offer promising methods to tackle some of these conventional feedback controller issues, but require large amounts of interaction data to learn. Here, we propose a hybrid learning and model-based controller Hybrid LMC that combines the strengths of a classical linear quadratic regulator (LQR) and ensemble deep reinforcement learning. Ensemble deep reinforcement learning is composed of multiple Soft Actor-Critic (SAC) and is utilized in reducing the variance of RL networks. By using a feedback controller in tandem the network exhibits stable performance in the early stages of training. As a preliminary step, we explore the viability of Hybrid LMC in controlling wheeled locomotion of a humanoid robot over a set of different physical parameters in MuJoCo simulator. Our results show that Hybrid LMC achieves better performance compared to other existing techniques and has increased sample efficiency