We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence
modeling problem. This allows us to draw upon the simplicity and scalability of the …

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

While behavior learning has made impressive progress in recent times, it lags behind
computer vision and natural language processing due to its inability to leverage large …

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

Diffusion models have emerged as powerful generative models in the text-to-image domain.
This paper studies their application as observation-to-action models for imitating human …

Goal-conditioned reinforcement learning endows an agent with a large variety of skills, but it
often struggles to solve tasks that require more temporally extended reasoning. In this work …

Recent advance in deep offline reinforcement learning (RL) has made it possible to train
strong robotic agents from offline datasets. However, depending on the quality of the trained …

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 …

Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …

As more non-AI experts use complex AI systems for daily tasks, there has been an
increasing effort to develop methods that produce explanations of AI decision making that …