BridgeData V2: A Dataset for Robot Learning at Scale

By Homer Rich Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Maximilian Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre Wang He and 4 more. Conference on Robot Learning, 2023

762 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Health Informatics

View Paper: https://www.semanticscholar.org/paper/6a375be82efc01ec4ed73334655935a56ba82d38

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success (2025, neutral): “As shown in Table XIV, the base OpenVLA pretrained representations are indeed still beneficial for robotic policy learning, as removing them leads to a 5.2% drop in average success rate (absolute) in our LIBERO evaluation suite.”
OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning (2024, background): “Recent efforts [40, 41, 42, 43, 44, 45, 46, 47] have focused on collecting robotic data using various embodiments for different tasks.”
Evaluating Real-World Robot Manipulation Policies in Simulation (2024, positive): “…introduced in Section IV to create SIMPLER (Simulated Manipulation Policy Evaluation for Real Robot Setups), a suite of simulated evaluation environments for commonly used real robot evaluation setups: the Google Robot from the RT series of works [6, 5, 11] and the WidowX BridgeV2 setup [66].”
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023, neutral): “We define the robotics data mixture used across all of the experiments as the data from 9 manipulators, and taken from RT-1 [8], QT-Opt [66], Bridge [95], Task Agnostic Robot Play [126, 127], Jaco Play [128], Cable Routing [129], RoboTurk [86], NYU VINN [130], Austin VIOLA [131], Berkeley Autolab…”
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators (2023, neutral): “As such, there are substantial ongoing efforts at collecting larger and larger datasets [49], [50].”
Learning Interactive Real-World Simulators (2023, background): “An increasing amount of video data of real-robot executions paired with task descriptions such as the Bridge Data [18] and data that enabled RT-1 and RT-2 [19] are becoming increasingly available.”
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models (2023, methodology): “Our dataset is BridgeData V2 [59], a large and diverse dataset of robotic manipulation behaviors designed for evaluating open-vocabulary instructions.”
UniVLA: Learning to Act Anywhere with Task-centric Latent Actions (2025, positive): “Empowered by the emergence of large-scale robotic datasets [78, 63, 38, 1], robot policies based on vision-language-action models (VLA) have made encouraging strides recently [10, 28, 39].”
SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model (2025, neutral): “IV are conducted on a mixture dataset that combines Google Fractal [6] and BridgeData V2 [64].”
Robotic Control via Embodied Chain-of-Thought Reasoning (2024, neutral): “Towards this goal, recent works have explored training “generalist robot policies” [10, 11, 12, 13, 14, 15, 16, 17] on diverse robot datasets [18, 19, 10, 20, 21, 22, 23, 24, 13, 14, 25, 15, 26, 6].”
π0.5: a Vision-Language-Action Model with Open-World Generalization (2025, positive): “…data distribution for robot manipulation policies from narrow, single-task datasets to diverse datasets that span many scenes and tasks [17, 25, 80, 63, 41, 6, 30, 67, 1] allows the resulting policies to not only solve a wider range of tasks out of the box, but also improves their ability to…”
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models (2025, neutral): “…evaluations across three complementary settings: the LIBERO benchmark [34] for evaluation in simulation environments, the Bridge-V2 platform [54] with its dataset of 45k robot demonstrations, and the Franka-Tabletop setup with a stationary, table-mounted Franka Emika Panda robot with…”
π0: A Vision-Language-Action Flow Model for General Robot Control (2024, neutral): “[52], and DROID [23].”