In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

This paper introduces Diffusion Policy, a new way of generating robot behavior by
representing a robot's visuomotor policy as a conditional denoising diffusion process. We …

Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …

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 …

We propose a new policy representation based on score-based diffusion models (SDMs).
We apply our new policy representation in the domain of Goal-Conditioned Imitation …

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

Training generalist agents capable of solving diverse tasks is challenging, often requiring
large datasets of expert demonstrations. This is particularly problematic in robotics, where …

Imitation learning from a large set of human demonstrations has proved to be an effective
paradigm for building capable robot agents. However, the demonstrations can be extremely …

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …

Human demonstration videos are a widely available data source for robot learning and an
intuitive user interface for expressing desired behavior. However, directly extracting …