Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions
October 16, 2024 Β· Declared Dead Β· π Conference on Robot Learning
"No code URL or promise found in abstract"
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Authors
Zhenyu Jiang, Yuqi Xie, Jinhan Li, Ye Yuan, Yifeng Zhu, Yuke Zhu
arXiv ID
2410.12773
Category
cs.RO: Robotics
Cross-listed
cs.AI
Citations
27
Venue
Conference on Robot Learning
Last Checked
4 months ago
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/.
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