HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid

Xinyu Xu, Yizheng Zhang, Yong-Lu Li, Lei Han, Cewu Lu

Keywords: Human-Scene Interaction; Object Rearrangement; Vision-Language-Action Model; Physical Humanoid

TL;DR: We introduce HumanVLA, which performs a varity of object rearrangement tasks directed by vision and language by physical humanoid.

Abstract:

Physical Human-Scene Interaction (HSI) plays a crucial role in numerous applications.
However, existing HSI techniques are limited to specific object dynamics and privileged information, which prevents the development of more comprehensive applications.
To address this limitation, we introduce HumanVLA for general object rearrangement directed by practical vision and language.
A teacher-student framework is utilized to develop HumanVLA.
A state-based teacher policy is trained first using goal-conditioned reinforcement learning and adversarial motion prior.
Then, it is distilled into a vision-language-action model via behavior cloning.
We propose several key insights to facilitate the large-scale learning process.
To support general object rearrangement by physical humanoid, we introduce a novel Human-in-the-Room dataset encompassing various rearrangement tasks.
Through extensive experiments and analysis, we demonstrate the effectiveness of our approach.

Primary Area: Reinforcement learning

Submission Number: 299