Large Video Planner Enables Generalizable Robot Control

Understand

General-purpose robots require decision-making models that generalize across diverse tasks and environments.

• Recent works build robot foundation models by extending multimodal large language models (MLLMs) with action outputs, creating vision-language-action (VLA) systems.
• These efforts are motivated by the intuition that MLLMs' large-scale language and image pretraining can be effectively transferred to the action output modality.
• In this work, we explore an alternative paradigm of using large-scale video pretraining as a primary modality for building robot foundation models.