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End of preview. Expand in Data Studio

SO101 Put [object] into Bowl

Dataset Summary

This dataset is formulated for use with the LeRobot framework. It contains robotic manipulation demonstrations for the SO101 embodiment performing a standardized pick-and-place task: "Put [object] into a bowl." The dataset captures precise manipulation trajectories across 10 distinct household and office objects against a fixed background, recorded from multiple camera angles. It is designed to train and evaluate visuomotor policies for robotic manipulation. Recorded and Validated by Sorrawit Poomseetong


Dataset Details

Statistics

  • Robot: SO101 Follower
  • Total Rows/Frames: 221,896
  • Control Frequency: 30 Hz
  • Demonstrations: 100 per object (1,000 total episodes)
  • Background Setup: Fixed background
  • Action Horizon/Length: ~200 steps per episode

Sensor Configuration

The dataset includes synchronized multi-view RGB image observations:

  • Top Camera: (observation.images.top) Provides a fixed, global overview of the workspace, the bowl, and the object's initial position.
  • Wrist Camera: (observation.images.wrist) Provides an ego-centric view from the robot's end-effector, crucial for fine manipulation and grasping tasks.

Task & Objects

Task Instruction: "Put [object] into a bowl."

The dataset covers variations of this task across the following 10 objects. Each object features exactly 100 successful demonstrations:

  • A plastic spoon
  • A metal spoon
  • A scissors
  • A roll of tissue paper
  • A blue marker
  • A black marker
  • A nipper
  • A glue
  • A white tape
  • A screw driver

Data Structure

Data is formatted natively for lerobot and contains the following standard feature keys per step:

Observations

  • observation.images.top (image): RGB image from the overhead camera.
  • observation.images.wrist (image): RGB image from the wrist camera.
  • observation.state (list of float32): The state of the SO101 robot.

Actions & Metadata

  • action (List[float32]): The commanded actions at 30 Hz.
  • timestamp (float32): Time elapsed in seconds since the start of the episode.
  • frame_index (int64): Frame number within the current episode.
  • episode_index (int64): Index of the demonstration episode (0 to 999).
  • index (int64): Global index of the frame.
  • task_index (int64): Corresponding ID for the specific object task.

Usage with LeRobot

You can load this dataset directly using the LeRobot library:

from lerobot.datasets.lerobot_dataset import LeRobotDataset

# Load the dataset
dataset = LeRobotDataset("cueng/so101_demo_bowl")

# Load first frame
frame = dataset[0]
print(frame.keys())
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