Authors: Zhenyu Jiang, Yuqi Xie, Jinhan Li, Ye Yuan, Yifeng Zhu, Yuke Zhu
Abstract: Humanoid robots, with their human-like embodiment, have the potential to
integrate seamlessly into human environments. Critical to their coexistence and
cooperation with humans is the ability to understand natural language
communications and exhibit human-like behaviors. This work focuses on
generating diverse whole-body motions for humanoid robots from language
descriptions. We leverage human motion priors from extensive human motion
datasets to initialize humanoid motions and employ the commonsense reasoning
capabilities of Vision Language Models (VLMs) to edit and refine these motions.
Our approach demonstrates the capability to produce natural, expressive, and
text-aligned humanoid motions, validated through both simulated and real-world
experiments. More videos can be found at
https://ut-austin-rpl.github.io/Harmon/.
Source: http://arxiv.org/abs/2410.12773v1
