By organizing demonstration videos into navigable hierarchies rather than static prompts, agents can access task details only when needed, reducing context overhead while improving learning from single examples.
This paper presents Recursive Video In-Context Learning (RV-ICL), a method that helps robot agents learn from demonstration videos more effectively. Instead of feeding entire videos as prompts, RV-ICL organizes a single demo video into a hierarchical structure of sub-events (like grasps and releases) that the agent can navigate on-demand.