di-techinnova/so-arm-101-pouring-0.1
This dataset contains 135 episodes of multi-task robotic manipulation focused on pouring and grasping activities. It was collected using a Leader-Follower (Master-Slave) setup with the SO-ARM-101 robotic arm (Waveshare/Koch Arm derivative). The dataset is designed to train and evaluate Vision-Language-Action (VLA) models (like SmolVLA or X-VLA) on tasks requiring high-precision visual grounding and long-horizon action sequences. Task Instructions The dataset covers distinct… See the full description on the dataset page: https://huggingface.co/datasets/di-techinnova/so-arm-101-pouring-0.1.
This dataset contains 135 episodes of multi-task robotic manipulation focused on pouring and grasping activities. It was collected using a Leader-Follower (Master-Slave) setup with the SO-ARM-101 robotic arm (Waveshare/Koch Arm derivative).
The dataset is designed to train and evaluate Vision-Language-Action (VLA) models (like SmolVLA or X-VLA) on tasks requiring high-precision visual grounding and long-horizon action sequences.
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Dataset Description
- Robot Type: SO-ARM-101 (6-DOF: 5 joints + 1 gripper)
- Control Frequency: 15 Hz
- Total Episodes: 135
- Total Frames: 65,250
- Visual Modalities:
camera1: Wrist-mounted camera (1280x720) for high-precision manipulation and object-centric views.camera2: Global/Portal view (640x360) using an Android phone camera for scene context.- Environment: Office meeting table (wood texture) with a high-contrast yellow background.
Task Instructions
The dataset covers distinct tasks across two main domains (Seeds and Coffee):
- Pouring Seeds: "Pour sunflower seeds from the orange cup into the clear cup."
- Pouring Coffee (Standard): "Pour coffee from the orange cup into the cup with the D sticker."
- Visual Grounding (High Contrast): "Pour coffee into the cup with the black-bordered letter D."
- Long-horizon Composition: "Pour coffee and then hold the D-marked cup." (A sequential task requiring a 0.5s pause between actions). ...
Dataset Structure
The data follows the LeRobot v3.0 format, using Parquet files for telemetry and MP4 files for video streams.
Features
Technical Details
Visual Grounding & Challenges
- Transparency Mitigation: To handle the challenges of transparent plastic cups, we utilized "Visual Anchors" including a white sticker with a black-bordered letter "D".
- Spatial Diversity: Episodes include variations in cup placement and camera angles to prevent overfitting to fixed coordinates.
- Temporal Consistency: Data was collected with careful attention to the 15Hz rhythm, ensuring actions and images are synchronized within a ~66ms window.
Action Space
The action space is continuous, representing the absolute angular positions of the servos. The gripper values typically range between 20-40 degrees for a firm hold and 60+ degrees for release.
How to use
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
# Load the dataset
dataset = LeRobotDataset("di-techinnova/so-arm-101-pouring-0.1")
# Access the first episode
frame = dataset[0]
image = frame["observation.images.camera1"]
state = frame["observation.state"]
action = frame["action"]
print(f"Instruction: {dataset.get_task(frame['task_index'])}")Citation
If you use this dataset in your research, please cite it as:
@misc{di-techinnova/so-arm-101-pouring-0.1,
author = {Data Impact VN - Technology Innovation Department},
title = {SO-ARM-101 Pouring Seeds and Coffee Dataset for VLA Training},
year = {2026},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.co/datasets/di-techinnova/so-arm-101-pouring-0.1}}
}