Whole-body Humanoid Robot Locomotion with Human Reference

TL;DR

This paper introduces a full-size humanoid robot named Adam and a novel adversarial imitation learning framework that enables human-like locomotion, achieving human-comparable performance in complex tasks.

Contribution

The paper presents a new humanoid robot design and a novel imitation learning framework based on adversarial motion prior, enhancing human-like locomotion capabilities.

Findings

Adam exhibits human-like locomotion behaviors.

The framework enables Adam to perform complex locomotion tasks.

First use of human locomotion data for imitation learning in a full-size humanoid.

Abstract

Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot, "Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot.