Embodied AI Datasets

This dataset contains 116,048 sets of 3D handpose data, each set includes hand mask image(RGB, 24-bit), depth image(16-bit), camera intrinsic parameter file(TXT), 3D keypoints file(OBJ), mesh file(OBJ), gesture type file(TXT), keypoints demo image(JPG), and mesh demo image(JPG). The data is collected indoors, with the right hand (no handheld objects), covering both first-person and third-person perspectives, multiple gesture types, finger poses, hand overall rotation poses, individuals and Kinect devices used. This dataset does not include personally identifiable facial information, with hand mask images and depth images aligned. This dataset can be used for tasks such as handpose recognition, hand 3D reconstruction, and hand keypoints detection.

This dataset contains 10,000 hours of egocentric multimodal data collected from diverse real-world environments, including residential, retail, and office scenarios. It covers a wide range of human activities and manipulation tasks, such as meal preparation, cleaning, storage, garment care, merchandising, and object picking. Each sample includes synchronized 4K stereo video, camera calibration parameters, 76-point full-body pose annotations, and fine-grained step-by-step action sequence labels. The dataset is suitable for robot learning, manipulation policy development, and Vision-Language-Action (VLA) models.

This dataset comprises 10,000 hours of dexterous manipulation data, capturing multimodal information across tasks such as grasping, translation, and rotation. It encompasses RGB-D visual streams, joint states, and force/torque feedback. Leveraging high-precision motion capture, the data ensures physical realism and temporal consistency in hand-object interactions, providing a robust foundation for training and validating algorithms in imitation learning, reinforcement learning, and robotic control.

This dataset contains 1,042 egocentric video segments (approximately 35 seconds each) collected across 34 locations and 6 real-world environments, including homes, offices, and retail scenarios. Powered by self-developed VSLAM system, achieving millimeter-level positioning, multi-sensor hard-triggered synchronization (≤1ms), and a high frame rate of 60fps for RGB. It includes multi-view videos, calibrations, point clouds, SLAM trajectories, and gesture recognition results in standard formats. Designed for embodied AI, spatial perception, and 3D reconstruction, it offers high precision, diverse scenarios, and out-of-the-box usability, making it ideal for training robust perception-action models.