Grounding Video Models to Actions through Goal Conditioned Exploration
Yunhao Luo Yilun Du
Large video models, pretrained on massive quantities of amount of Internet video,  provide a rich source of physical knowledge about the dynamics and motions of objects and tasks.
However, video models are not grounded in the embodiment of an agent, and do not describe how to actuate the world to reach the visual states depicted in a video.
To tackle this problem, current methods use a separate vision-based inverse dynamic model trained on embodiment-specific data to map image states to actions. 
Gathering data to train such a model is often expensive and challenging, and this model is limited to visual settings similar to the ones in which data is available.
In this paper, we investigate how to directly  ground video models to continuous actions through self-exploration in the embodied environment -- using generated video states as visual goals for exploration.
We propose a framework that uses trajectory level action generation in combination with video guidance to
enable an agent to solve complex tasks without any external supervision, e.g., rewards, action labels, or segmentation masks.
We validate the proposed approach on 8 tasks in Libero, 6 tasks in MetaWorld, 4 tasks in Calvin, and 12 tasks in iThor Visual Navigation. 
We show how our approach is on par with or even surpasses multiple behavior cloning baselines trained on expert demonstrations while without requiring any action annotations.
Embodied AI, Decision Making, Robotics, Video Model
We illustrate how we can ground video models to actions without using actions labels through goal conditioned exploration.