Datasets:
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
This dataset was created using LeRobot.
Dataset Description
This dataset contains the final batch of teleoperated demonstrations collected during a two-day hackathon using the LeRobot library and SO-101 robot arms in a leader–follower configuration. Each episode shows the follower arm picking two colored cubes (one after the other) and placing each into the matching colored cross within a 2×2 grid. Two RGB cameras were used:
Top camera: mounted above the workspace for a clear 2D view of the arm, cubes, and grid.
Front/low camera: slightly above the ground, facing the arm and grid to provide better z-axis cues and arm self-perception.
Despite cardboard backgrounding, the room’s illumination varied over time and is deliberately preserved in the data, as it proved to be a limiting factor and may be valuable for robustness research.
This dataset is intended for vision-based imitation learning (e.g., behavior cloning, goal-conditioned policies), multi-view fusion, and perception-control studies on tabletop manipulation.
Use Cases
Imitation Learning: Behavior cloning from teleop demonstrations.
Multiview Perception: Fusing top + front perspectives for depth inference without explicit depth sensors.
Robustness to Lighting: Evaluating policy sensitivity to illumination drift.
State–Action Alignment: Leveraging synchronized proprioception and images.
Data Collection
Teleoperation Setup
Leader–Follower: Human teleoperates a leader arm; follower SO-101 replicates motion to generate demonstrations.
Workspace: Tabletop with a 2×2 grid. Each cell contains a colored cross; two colored cubes must be placed on matching crosses.
Cameras:
Top: overhead, full scene.
Front: low angle, emphasizes depth and arm self-pose.
Background control: Cardboard panels; lighting varies during the day and is preserved in data.
Episode Protocol
1- Move to pre-grasp; localize target cube(s) visually.
2- Grasp first cube; transport; place on correct colored cross.
3- Repeat for second cube.
4- Return to neutral.
Known limitations
Lighting drift: Significant variation during the day; expect distribution shift. Consider color constancy or data augmentation.
Camera motion: Cameras are fixed for the batch, but small nudges may occur; rely on metadata intrinsics/extrinsics if provided.
Occlusions: Self-occlusion of the gripper and cubes in certain positions, especially from left camera during close approach.
No depth: RGB only
Additional Information
Homepage: deel-ai
License: apache-2.0
Dataset Structure
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 50,
"total_frames": 31189,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6
]
},
"observation.state": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
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},
"observation.images.left": {
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"shape": [
480,
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3
],
"names": [
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],
"info": {
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"video.width": 640,
"video.codec": "av1",
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"video.is_depth_map": false,
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"video.channels": 3,
"has_audio": false
}
},
"observation.images.front": {
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"info": {
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"video.is_depth_map": false,
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"has_audio": false
}
},
"timestamp": {
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},
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"episode_index": {
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"index": {
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}