In this study, we are interested in imbuing robots with the capability of physically-grounded
task planning. Recent advancements have shown that large language models (LLMs) …

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …

Reward and representation learning are two long-standing challenges for learning an
expanding set of robot manipulation skills from sensory observations. Given the inherent …

TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local
trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In …

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 …

In a wide range of multimodal tasks, contrastive learning has become a particularly
appealing approach since it can successfully learn representations from abundant …

Despite recent progress in reinforcement learning (RL) from raw pixel data, sample
inefficiency continues to present a substantial obstacle. Prior works have attempted to …

Given time series data, how can we answer questions like what will happen in the
future?''and how did we get here?''These sorts of probabilistic inference questions are …

Passive observational data, such as human videos, is abundant and rich in information, yet
remains largely untapped by current RL methods. Perhaps surprisingly, we show that …