Think on your feet: Seamless Transition between Human-like Locomotion in Response to Changing Commands
Journal:
arXiv
Published Date:
Feb 26, 2025
Abstract
While it is relatively easier to train humanoid robots to mimic specific
locomotion skills, it is more challenging to learn from various motions and
adhere to continuously changing commands. These robots must accurately track
motion instructions, seamlessly transition between a variety of movements, and
master intermediate motions not present in their reference data. In this work,
we propose a novel approach that integrates human-like motion transfer with
precise velocity tracking by a series of improvements to classical imitation
learning. To enhance generalization, we employ the Wasserstein divergence
criterion (WGAN-div). Furthermore, a Hybrid Internal Model provides structured
estimates of hidden states and velocity to enhance mobile stability and
environment adaptability, while a curiosity bonus fosters exploration. Our
comprehensive method promises highly human-like locomotion that adapts to
varying velocity requirements, direct generalization to unseen motions and
multitasking, as well as zero-shot transfer to the simulator and the real world
across different terrains. These advancements are validated through simulations
across various robot models and extensive real-world experiments.
