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So-101 Dataset
The dataset comprises 20 episodes of high-quality teleoperated robotic arm recordings at ~30 FPS. It is a compact, curated set captured using multi-view cameras (front, wrist, top) within the calibrated robot studio of the SO-101 arm, leveraging the LeRobot framework. Specifically designed for imitation learning and model training on the LeRobot platform, this dataset demonstrates basic manipulation and pick-and-place tasks with structured LeRobot v2.1 annotations.
By utilizing this robotic dataset, researchers and developers can advance evaluation of pretrained models, address issues in setups, and explore calibrations for robotics applications facing real-world tasks. - Get the data
Its core purpose is to support imitation learning, model training, and the evaluation of pretrained models. This compact set of 20 teleoperated robotic arm recordings is designed for imitation learning, model training, and training models on the lerobot platform, demonstrating pick-and-place and basic manipulation tasks with structured LeRobot v2.1 annotations across robots, arms, platforms, frameworks, and tools.
Frequently Asked Questions
What robot platform was used to collect the data?
The dataset was recorded using the SO-101 robotic arm, a 5-degree-of-freedom (5-DOF) manipulator with a gripper. All demonstrations were captured in a calibrated robot studio, providing consistent observations for imitation learning and robotics research.
How was the dataset collected?
The demonstrations were recorded through teleoperation, where a human operator controlled the SO-101 robotic arm to perform manipulation tasks. This approach produces realistic action trajectories that are well suited for imitation learning and behavior cloning.
What file formats are included?
The dataset follows the LeRobot v2.1 specification and includes MP4 videos together with Parquet files containing robot observations, actions, and metadata. This standardized format enables straightforward integration into LeRobot-based robotics pipelines."
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Researchers can leverage this dataset to optimize training on the lerobot platform, tackle issues in robotic frameworks, and build robust models for robots and arms.
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