BridgeData V2: A Dataset for Robot Learning at Scale

Homer Rich Walke, Kevin Black, Tony Z. Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, Abraham Lee, Kuan Fang, Chelsea Finn, Sergey Levine

Keywords: Datasets, Manipulation, Imitation Learning, Offline Reinforcement Learning

TL;DR: We introduce a new dataset of robotic behaviors designed to facilitate research in large-scale robot learning.

Abstract: 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 trajectories collected across 24 environments on a publicly available low-cost robot. Unlike many existing robotic manipulation datasets, BridgeData V2 provides enough task and environment variability that skills learned from the data generalize across 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 apply 6 state-of-the-art imitation learning and offline reinforcement learning methods to the data 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. By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Website: https://rail-berkeley.github.io/bridgedata/

Code: https://github.com/rail-berkeley/bridge_data_v2