As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a
learning machine) is trained to perform a task from demonstrations by learning a map** …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

Learning from unstructured and uncurated data has become the dominant paradigm for
generative approaches in language or vision. Such unstructured and unguided behavior …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

Dexterous multi-fingered hands are extremely versatile and provide a generic way to
perform a multitude of tasks in human-centric environments. However, effectively controlling …

Recent work has shown promising results for learning end-to-end robot policies using
imitation learning. In this work we address the question of how far can we push imitation …

Reinforcement learning holds the promise of enabling autonomous robots to learn large
repertoires of behavioral skills with minimal human intervention. However, robotic …

We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop
visuomotor policies for robot manipulation. Our approach constructs object-centric …

We tackle the problem of learning complex, general behaviors directly in the real world. We
propose an approach for robots to efficiently learn manipulation skills using only a handful of …