Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
Humans exhibit diverse and expressive whole-body movements. However,
attaining human-like whole-body coordination in humanoid robots remains
challenging, as conventional approaches that mimic whole-body motions often
neglect the distinct roles of upper and lower body. This oversight leads to
computationally intensive policy learning and frequently causes robot
instability and falls during real-world execution. To address these issues, we
propose Adversarial Locomotion and Motion Imitation (ALMI), a novel framework
that enables adversarial policy learning between upper and lower body.
Specifically, the lower body aims to provide robust locomotion capabilities to
follow velocity commands while the upper body tracks various motions.
Conversely, the upper-body policy ensures effective motion tracking when the
robot executes velocity-based movements. Through iterative updates, these
policies achieve coordinated whole-body control, which can be extended to
loco-manipulation tasks with teleoperation systems. Extensive experiments
demonstrate that our method achieves robust locomotion and precise motion
tracking in both simulation and on the full-size Unitree H1 robot.
Additionally, we release a large-scale whole-body motion control dataset
featuring high-quality episodic trajectories from MuJoCo simulations deployable
on real robots. The project page is https://almi-humanoid.github.io.
