Jose Barreiros

Abstract Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently, scaling data and model size has led to the development of capable language and vision foundation models, motivating large-scale efforts to create general-purpose robot foundation models.

Abstract Teaching robots new skills should be as natural as showing rather than programming. Learning from demonstration (LfD) moves toward this goal by allowing users to guide a robot or sketch a desired motion, enabling learning without writing a line of code. However, most LfD methods remain tied to the robot they were trained on. Changes in morphology, different link lengths, joint orientations, or limits often break the learned behavior, making retraining unavoidable.

Abstract Robotic manipulation remains one of the most difficult challenges in robotics, with approaches ranging from classical model-based control to modern imitation learning. Although these methods have enabled substantial progress, they often require extensive manual design, struggle with performance, and demand large-scale data collection. These limitations hinder their real-world deployment at scale, where reliability, speed, and robustness are essential.