Learning Real-World Action-Video Dynamics with Heterogeneous Masked
  Autoregression
Authors: name: Lirui Wang, name: Kevin Zhao, name: Chaoqi Liu, name: Xinlei Chen

We propose Heterogeneous Masked Autoregression (HMA) for modeling
action-video dynamics to generate high-quality data and evaluation in scaling
robot learning. Building interactive video world models and policies for
robotics is difficult due to the challenge of handling diverse settings while
maintaining computational efficiency to run in real time. HMA uses
heterogeneous pre-training from observations and action sequences across
different robotic embodiments, domains, and tasks. HMA uses masked
autoregression to generate quantized or soft tokens for video predictions.
\ourshort achieves better visual fidelity and controllability than the previous
robotic video generation models with 15 times faster speed in the real world.
After post-training, this model can be used as a video simulator from low-level
action inputs for evaluating policies and generating synthetic data. See this
link https://liruiw.github.io/hma for more information.