The thesis proposal is organized into two parts: (1) completed and ongoing work focusing on video, language, and audio modalities, and (2) proposed work combining multiple modalities to fast adaptation of Robotics Foundation Models like OpenVLA. First, I present how we can train robot policies to infer the underlying tasks and preferences from the visual demonstration. I show how to infer the implicit task shown with cross-attention transformers to perform simulated dish-loading tasks. Then, I apply the same philosophy to train policies that infer the underlying task semantics from raw pixels in a prompt video and execute it in the robot’s own environment. Second, I challenge the strong assumptions about language-based goal conditioning in robot policies.  In one of the main works, I present sample-efficient robot policies that use the hierarchical decomposition of language into a sequence of interaction points and their relative waypoints.