Natural Humanoid Robot Locomotion with Generative Motion Prior
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
Natural and lifelike locomotion remains a fundamental challenge for humanoid
robots to interact with human society. However, previous methods either neglect
motion naturalness or rely on unstable and ambiguous style rewards. In this
paper, we propose a novel Generative Motion Prior (GMP) that provides
fine-grained motion-level supervision for the task of natural humanoid robot
locomotion. To leverage natural human motions, we first employ whole-body
motion retargeting to effectively transfer them to the robot. Subsequently, we
train a generative model offline to predict future natural reference motions
for the robot based on a conditional variational auto-encoder. During policy
training, the generative motion prior serves as a frozen online motion
generator, delivering precise and comprehensive supervision at the trajectory
level, including joint angles and keypoint positions. The generative motion
prior significantly enhances training stability and improves interpretability
by offering detailed and dense guidance throughout the learning process.
Experimental results in both simulation and real-world environments demonstrate
that our method achieves superior motion naturalness compared to existing
approaches. Project page can be found at
https://sites.google.com/view/humanoid-gmp
