Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains

TL;DR

This paper presents a hybrid control architecture combining reinforcement learning and model-predictive control to enable humanoid robots to adaptively navigate various terrains efficiently, reducing training data needs and enhancing transferability.

Contribution

The novel integration of RL-trained high-level policies with advanced motion control enables terrain-adaptive locomotion in humanoids with fewer training samples and better platform transferability.

Findings

Achieved dynamic locomotion across discrete terrains in simulations.

Reduced training data requirements compared to traditional RL methods.

Successfully transferred control architecture to different humanoid robots.

Abstract

This paper addresses the challenge of terrain-adaptive dynamic locomotion in humanoid robots, a problem traditionally tackled by optimization-based methods or reinforcement learning (RL). Optimization-based methods, such as model-predictive control, excel in finding optimal reaction forces and achieving agile locomotion, especially in quadruped, but struggle with the nonlinear hybrid dynamics of legged systems and the real-time computation of step location, timing, and reaction forces. Conversely, RL-based methods show promise in navigating dynamic and rough terrains but are limited by their extensive data requirements. We introduce a novel locomotion architecture that integrates a neural network policy, trained through RL in simplified environments, with a state-of-the-art motion controller combining model-predictive control (MPC) and whole-body impulse control (WBIC). The policy efficiently learns high-level locomotion strategies, such as gait selection and step positioning, without the need for full dynamics simulations. This control architecture enables humanoid robots to dynamically navigate discrete terrains, making strategic locomotion decisions (e.g., walking, jumping, and leaping) based on ground height maps. Our results demonstrate that this integrated control architecture achieves dynamic locomotion with significantly fewer training samples than conventional RL-based methods and can be transferred to different humanoid platforms without additional training. The control architecture has been extensively tested in dynamic simulations, accomplishing terrain height-based dynamic locomotion for three different robots.