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BridgeData V2: A Dataset for Robot Learning at Scale
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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
Forward citations
Cited by 12 Pith papers
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GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert
A frozen, large-scale pretrained diffusion policy converts sparse 3D waypoints from a VLM into dense robot actions, enabling zero-shot reuse of the action expert on new tasks and environments.
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Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
A taxonomy of robot learning on a weights-versus-skills axis, with a five-rung self-improvement ladder whose top cell (feedback plus memory plus search) holds only a few recent systems.
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HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
Robot-free HiFi-UMI demonstrations can replace teleoperated real-robot data in post-training: three policy backbones matched in-domain teleoperation within 3.1 percentage points, including 85% success on a precision i...
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ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
Aligning temporal granularity, action subspaces, and train-test conditioning yields SOTA long-horizon mobile and fine-grained manipulation success for a unified world-action model.
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DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
A decoupled multimodal diffusion transformer with a LoRA tactile adapter improves real-world bimanual manipulation success by 21 percentage points over a diffusion-policy baseline; a new 50-hour tactile bimanual datas...
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Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation
Slot-based object-centric visual representations, especially with robot-video pretraining, improve out-of-distribution generalization of robotic manipulation policies compared to global and dense pre-trained features.
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INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
Token-level uncertainty sequences from a VLA policy, classified by a small transformer, predict when a robot should request human help better than static uncertainty scores.
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SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning
SCIZOR filters suboptimal and redundant state-action pairs from robot demonstrations without human labels, improving imitation-learning policy success rates by about 15% on average.
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iFLYTEK-Embodied-Omni Technical Report
A three-branch Omni model (VLM+VGM brain, AGM cerebellum) with shared multimodal attention and four-stage training reaches 89.6% zero-shot on LIBERO-Plus and ~93% on RoboTwin 2.0.
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DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalization
Fusing RGB and point-cloud inputs with training-time modality dropout plus cross-attention makes a diffusion visuomotor policy markedly more robust to visual and spatial shifts than unimodal or naively fused baselines.
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3D-Grounded Vision-Language Framework for Robotic Task Planning: Automated Prompt Synthesis and Supervised Reasoning
A 2D prompt synthesis and small-language-model supervision framework is reported to reach 96% task success on Franka headphone manipulation tasks, though reproducibility artifacts are absent.
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ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver
Adding a reconstruction target that redraws the object region makes a vision-language-action model focus its attention on the right object and manipulate more precisely.
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