  We introduce BridgeData V2, a large and diverse dataset of robotic
manipulation behaviors designed to facilitate research on scalable robot
learning. BridgeData V2 contains 60,096 trajectories collected across 24
environments on a publicly available low-cost robot. BridgeData V2 provides
extensive task and environment variability, leading to skills that can
generalize across environments, domains, and institutions, making the dataset a
useful resource for a broad range of researchers. Additionally, the dataset is
compatible with a wide variety of open-vocabulary, multi-task learning methods
conditioned on goal images or natural language instructions. In our
experiments, we train 6 state-of-the-art imitation learning and offline
reinforcement learning methods on our dataset, and find that they succeed on a
suite of tasks requiring varying amounts of generalization. We also demonstrate
that the performance of these methods improves with more data and higher
capacity models, and that training on a greater variety of skills leads to
improved generalization. By publicly sharing BridgeData V2 and our pre-trained
models, we aim to accelerate research in scalable robot learning methods.
Project page at https://rail-berkeley.github.io/bridgedata
