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This paper presents FineART, a densely annotated bimanual manipulation dataset with 40,543 episodes and 533,913 subtask labels, totaling 1,718 hours. It also introduces FineART-VLA, a vision-language-action policy that improves task success rates through dense subtask training. The dataset and model are open-sourced, offering a new benchmark for long-horizon bimanual manipulation tasks.
The GrandTour dataset addresses the lack of large-scale, real-world legged-robot data by providing time-synchronized sensor data from multiple modalities, including LiDAR, RGB cameras, depth sensors, and GNSS. It supports research in SLAM, state estimation, and multi-modal learning, with data available in ROS and HuggingFace formats.
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