Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.12952 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913199296610304 |
|---|---|
| author | Walke, Homer Black, Kevin Lee, Abraham Kim, Moo Jin Du, Max Zheng, Chongyi Zhao, Tony Hansen-Estruch, Philippe Vuong, Quan He, Andre Myers, Vivek Fang, Kuan Finn, Chelsea Levine, Sergey |
| author_facet | Walke, Homer Black, Kevin Lee, Abraham Kim, Moo Jin Du, Max Zheng, Chongyi Zhao, Tony Hansen-Estruch, Philippe Vuong, Quan He, Andre Myers, Vivek Fang, Kuan Finn, Chelsea Levine, Sergey |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_12952 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BridgeData V2: A Dataset for Robot Learning at Scale Walke, Homer Black, Kevin Lee, Abraham Kim, Moo Jin Du, Max Zheng, Chongyi Zhao, Tony Hansen-Estruch, Philippe Vuong, Quan He, Andre Myers, Vivek Fang, Kuan Finn, Chelsea Levine, Sergey Robotics Machine Learning 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 |
| title | BridgeData V2: A Dataset for Robot Learning at Scale |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2308.12952 |