ly152/simpleicl-sample

SimpleICL Real-Robot Data Sample

A sample of our paired human-robot manipulation data, released alongside SimpleICL — the official code release of the paper In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks. It is a subset of the full dataset collected for the project, provided to illustrate the data format and to accompany the training recipe in the repository README (Training).

Dataset Summary

100 paired episodes of bimanual Franka FR3 tabletop manipulation. In every pair, a teleoperated robot arm and a human hand perform the same task back to back; the human-side demonstration video serves as the in-context condition (video_ctx) that the policy consumes at training and inference time.

Episodes	100 pairs (100 robot + 100 human)
Total frames (robot side)	28,457 @ 30 fps
Episode length	157–449 frames
Robot side	3 RealSense cameras (high + left/right wrist), 848×480
Human side	1 high camera, 848×480
Action / state	16-D (7 joints + 1 gripper per arm)

Scene alterations keep the task unchanged and perturb only task-irrelevant factors — object positions, lighting, and background colors. The same task recorded under the base and altered placements references each other as in-context conditions (see robot/meta/conditions_list.json), so a policy trained on this data learns what the task is from the demonstration video while staying robust to where objects sit, how the scene is lit, and what the background looks like.

Structure

The data is distributed as a single archive, pair_data.zip. Unzipping it produces two sibling LeRobot-format (v2.1) dataset directories:

pair_data.zip
└── (extracts to:)
    ├── robot/                      # teleoperated robot episodes
    │   ├── meta/                   # tasks, episodes, scenes, conditions
    │   │   ├── tasks.jsonl
    │   │   ├── episodes.jsonl
    │   │   ├── episodes_stats.jsonl
    │   │   ├── info.json
    │   │   ├── scenes.json          # episode index -> scene placement label
    │   │   ├── plan_rows.json
    │   │   ├── conditions.json
    │   │   └── conditions_list.json # episode index -> human demo videos (video_ctx)
    │   ├── data/                   # parquet per episode
    │   └── videos/                 # 3 cameras per episode
    └── human/                      # human-hand demonstrations
        └── videos/                 # cam_high video per episode

Condition paths in robot/meta/conditions_list.json are relative to the extraction root, so the dataset keeps working after the extracted directory is moved as a whole.

Usage

Download the archive, unzip it, and point the SimpleICL training config at the robot side — see the repository SimpleICL README for the one-command training flow.

Additional Information

• License: MIT
• Origin: a sample subset of the data collected by the SimpleICL authors on a dual-arm Franka FR3 platform with RealSense cameras.
• Associated code: SimpleICL
• Citation: see below.

Citation

@article{simpleicl2026,
  title={In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks},
  author={Min-Xing Li and Ming-Hao Han and Wei-Zhi Zhao and Han-Wen Wang and Xiang-Shuo Liu and Shu-Yao Shang and Jing-Xiang Zhou and Ming-Chao Sun and Hong-Yu Pan and Mu Xu and Yu Liu and Lue Fan and Zhao-Xiang Zhang},
  year={2026}
}