  Sequential decision-making can be formulated as a text-conditioned video
generation problem, where a video planner, guided by a text-defined goal,
generates future frames visualizing planned actions, from which control actions
are subsequently derived. In this work, we introduce Active Region Video
Diffusion for Universal Policies (ARDuP), a novel framework for video-based
policy learning that emphasizes the generation of active regions, i.e.
potential interaction areas, enhancing the conditional policy's focus on
interactive areas critical for task execution. This innovative framework
integrates active region conditioning with latent diffusion models for video
planning and employs latent representations for direct action decoding during
inverse dynamic modeling. By utilizing motion cues in videos for automatic
active region discovery, our method eliminates the need for manual annotations
of active regions. We validate ARDuP's efficacy via extensive experiments on
simulator CLIPort and the real-world dataset BridgeData v2, achieving notable
improvements in success rates and generating convincingly realistic video
plans.
