  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 the proposed approach.
