SharpaIT/RoCo_TaskBoardAssembly

RoCo Task Board Assembly Demonstrations

Real-world LeRobot demonstrations for contact-rich task-board assembly.

Overview

RoCo Task Board Assembly Demonstrations is a real-world robot manipulation dataset for assembling parts on a task board. It is released by Sharpa in a LeRobot-compatible format for imitation learning, visuomotor policy learning, visual-tactile representation learning, and contact-rich manipulation research.

Task-board assembly requires precise part localization, fine contact timing, bimanual coordination, and robust perception under hand-object occlusion. The demonstrations include synchronized multi-view video, tactile observations, proprioceptive state, torque signals, and action targets.

This dataset supports the RoCo IROS 2026 Challenge. Teams can use it to develop, train, and evaluate policies for the task-board assembly track. Interested participants should visit the official competition website and complete the registration form to receive challenge updates and participation details.

Task Task-board part assembly	Format LeRobot v3.0 / v2.1	Scale 30 seasons / 562 episodes	Frequency 30 FPS
Video 6 synchronized streams	State / Action 65D joint space	Tactile 60D signal + tactile video	Use Training and policy development

Competition and Registration

The RoCo IROS 2026 Challenge provides a shared benchmark for real-world robotic assembly, focusing on contact-rich manipulation, bimanual coordination, visual-tactile perception, and robust policy execution. The task-board assembly dataset is released as training data for teams participating in the challenge and for researchers working on related manipulation problems.

• Competition website: https://rocochallenge.github.io/RoCo-IROS2026/
• Registration form: https://forms.gle/d2NKNAE7dqSfYZB87

Please refer to the competition website for the latest schedule, rules, evaluation details, and participation instructions.

Dataset Capabilities

Capability	Dataset Support
Contact-rich assembly learning	Real-world demonstrations for assembling task-board parts
Multi-view visuomotor policies	Synchronized head-camera and wrist-camera observations
Visual-tactile learning	High-resolution tactile videos, synchronized raw tactile camera views, and 10-fingertip 6-axis tactile signals
Joint-space control	65D synchronized state and action for two arms, two dexterous hands, and torso/motor-related joints
LeRobot ecosystem	lerobot3.0 and lerobotv2.1 exports for every released season

Example Views

The demonstrations include synchronized head, wrist, and tactile video streams. Each preview below uses a representative window from the same lerobotv2.1 episode, played at 10x speed. GIF previews render directly in Markdown; click any preview to open the MP4 version. Tactile previews preserve their full wide-frame layout.

Head Left	Head Right
Wrist Left	Wrist Right
Tactile Deformation	Raw Tactile

Dataset Statistics

Item	Value
Total collection seasons	30
lerobot3.0 seasons	30
lerobot3.0 episodes	562
lerobot3.0 frames	2,461,024
lerobotv2.1 seasons	30
lerobotv2.1 episodes	562
lerobotv2.1 frames	2,461,024
FPS	30
Video streams	6
State/action dimension	65
Tactile signal dimension	60
Approximate data size	324.3 GB

For new users, we recommend starting from lerobot3.0. The lerobotv2.1 export is included for compatibility with pipelines that still depend on the older LeRobot layout.

Get Started

Download The Dataset

Make sure Git LFS is installed before cloning from Hugging Face.

git lfs install
git clone https://huggingface.co/datasets/SharpaIT/RoCo_TaskBoardAssembly

If you want to clone only metadata first and fetch large files later:

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/SharpaIT/RoCo_TaskBoardAssembly

If you only want a specific season, use sparse checkout:

git init RoCo_TaskBoardAssembly
cd RoCo_TaskBoardAssembly
git remote add origin https://huggingface.co/datasets/SharpaIT/RoCo_TaskBoardAssembly
git sparse-checkout init
git sparse-checkout set season_POC22061_2026_06_11_14_29_08_train README.md
git pull origin main

Quick Inspection

Each season contains both lerobot3.0 and lerobotv2.1 exports. Inspect meta/info.json first to understand the exact schema and file templates.

import json
from pathlib import Path

dataset_root = Path("RoCo_TaskBoardAssembly")
episode_root = dataset_root / "season_POC22061_2026_06_11_14_29_08_train" / "lerobot3.0"

with open(episode_root / "meta" / "info.json", "r") as f:
    info = json.load(f)

print(info["total_episodes"])
print(info["total_frames"])
print(info["features"].keys())

Dataset Structure

The dataset is organized by collection season. Each season contains a lerobot3.0 export and a lerobotv2.1 export.

RoCo_TaskBoardAssembly/
├── README.md
├── season_POC22061_2026_06_11_14_29_08_train/
│   ├── lerobot3.0/
│   │   ├── meta/
│   │   │   ├── info.json
│   │   │   ├── modality.json
│   │   │   ├── episodes/
│   │   │   └── tasks.parquet
│   │   ├── data/
│   │   │   └── chunk-000/
│   │   └── videos/
│   │       ├── observation.images.head_left/
│   │       ├── observation.images.head_right/
│   │       ├── observation.images.wrist_left/
│   │       ├── observation.images.wrist_right/
│   │       ├── observation.images.tactile_deform/
│   │       └── observation.images.tactile_raw/
│   └── lerobotv2.1/
│       ├── meta/
│       ├── data/
│       └── videos/
└── season_.../

LeRobot Storage Layout

Part	Description
meta/	Dataset metadata, feature schema, task metadata, and path templates
data/	Episode frame data stored as Apache Parquet files
videos/	Per-camera MP4 videos

The most important metadata file is meta/info.json. It defines total_episodes, total_frames, fps, splits, data_path, video_path, and features.

File Path Templates

LeRobot v3.0 uses templates similar to:

data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet
videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4

LeRobot v2.1 uses templates similar to:

data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

Features Schema

Main Feature Groups

Feature	Type	Shape	Description
observation.state	float32	65	Joint-space robot state
action	float32	65	Joint-space action
observation.state.joint_torque	float32	65	Joint torque signal
observation.tactile	float32	60	Tactile force/torque signal
observation.images.*	video	varies	Multi-view visual observations
timestamp	float32	1	Timestamp
frame_index	int64	1	Frame index within an episode
episode_index	int64	1	Episode index
task_index	int64	1	Task index

Proprioceptive State

The 65D observation.state and action vectors are ordered as follows:

Range	Names	Meaning
0 - 6	left_arm_j0 to left_arm_j6	Left arm joints
7 - 28	left_hand_j0 to left_hand_j21	Left dexterous hand joints
29 - 35	right_arm_j0 to right_arm_j6	Right arm joints
36 - 57	right_hand_j0 to right_hand_j21	Right dexterous hand joints
58 - 64	motor_j0 to motor_j6	Torso / motor-related joints

Video Streams

The complete visual observation set contains six camera streams:

Feature key	Description	Shape
observation.images.head_left	Left head camera	480 x 480 x 3
observation.images.head_right	Right head camera	480 x 480 x 3
observation.images.wrist_left	Left wrist camera	480 x 480 x 3
observation.images.wrist_right	Right wrist camera	480 x 480 x 3
observation.images.tactile_deform	Tactile deformation video	480 x 1200 x 3
observation.images.tactile_raw	Raw tactile video	480 x 1600 x 3

Video files are MP4 without audio. Codec may differ across exports and seasons. lerobot3.0 is primarily AV1, while lerobotv2.1 is primarily H.264.

Tactile Modality

The dataset includes tactile observations as both compact numeric signals and high-resolution video streams. These modalities are synchronized with the robot state, action, and visual camera streams at 30 FPS, making them suitable for contact-rich policy learning and visual-tactile representation learning.

Tactile feature	Type	Shape	Description
observation.tactile	float32	60	Per-frame tactile force/torque signal: 10 fingertips x 6 axes
observation.images.tactile_deform	video	480 x 1200 x 3	Deformation-oriented tactile video stream that visualizes contact-induced surface changes
observation.images.tactile_raw	video	480 x 1600 x 3	Raw tactile camera stream preserving the full tactile sensor image layout

The observation.tactile vector provides a compact force/torque representation for each frame. It contains 10 fingertip groups: left and right thumb, index, middle, ring, and little. Each fingertip contributes six values, ordered as fx, fy, fz, tx, ty, and tz, for a total of 60 dimensions.

The two tactile video streams provide complementary image-based tactile observations: tactile_deform emphasizes deformation patterns caused by contact, while tactile_raw preserves the raw tactile image for users who want to build their own visual-tactile preprocessing or representation learning pipeline. For downstream experiments, users can start with observation.tactile as a lightweight contact signal, then add one or both tactile video streams when the model architecture can handle the additional spatial resolution and bandwidth.

Season List

Season	lerobot3.0	lerobotv2.1
season_POC22061_2026_06_11_14_29_08_train	3.39 GB	3.40 GB
season_POC22061_2026_06_11_19_10_57_train	3.45 GB	3.46 GB
season_POC22061_2026_06_11_20_21_30_train	4.76 GB	4.78 GB
season_POC22061_2026_06_14_10_25_39_train	3.82 GB	3.83 GB
season_POC22061_2026_06_14_15_44_09_train	7.40 GB	7.43 GB
season_POC22061_2026_06_15_10_15_11_train	4.07 GB	4.08 GB
season_POC22061_2026_06_15_11_15_27_train	2.91 GB	2.92 GB
season_POC22061_2026_06_15_15_56_02_train	9.96 GB	10.00 GB
season_POC22061_2026_06_15_19_24_12_train	6.47 GB	6.49 GB
season_POC22061_2026_06_16_10_36_10_train	2.47 GB	2.48 GB
season_POC22061_2026_06_16_13_39_56_train	7.77 GB	7.79 GB
season_POC22061_2026_06_16_15_59_28_train	6.01 GB	6.03 GB
season_POC22061_2026_06_16_19_09_01_train	5.15 GB	5.17 GB
season_POC22061_2026_06_17_10_36_58_train	6.48 GB	6.50 GB
season_POC22061_2026_06_17_13_36_51_train	6.02 GB	6.04 GB
season_POC22061_2026_06_17_15_33_05_train	7.65 GB	7.68 GB
season_POC22061_2026_06_17_19_15_29_train	5.62 GB	5.64 GB
season_POC22061_2026_06_18_10_08_09_train	6.11 GB	6.13 GB
season_POC22061_2026_06_18_13_41_46_train	5.31 GB	5.33 GB
season_POC22061_2026_06_18_19_11_08_train	6.76 GB	6.78 GB
season_POC22061_2026_06_18_20_41_26_train	3.66 GB	3.68 GB
season_POC22061_2026_06_19_10_02_47_train	3.16 GB	3.17 GB
season_POC22061_2026_06_19_10_45_34_train	3.53 GB	3.54 GB
season_POC22061_2026_06_19_13_51_05_train	5.85 GB	5.87 GB
season_POC22061_2026_06_19_16_00_30_train	5.91 GB	5.93 GB
season_POC22061_2026_06_19_19_14_55_train	5.61 GB	5.63 GB
season_POC22061_2026_06_20_10_09_11_train	6.21 GB	6.23 GB
season_POC22061_2026_06_20_13_43_19_train	5.80 GB	5.82 GB
season_POC22061_2026_06_20_15_57_28_train	5.60 GB	5.62 GB
season_POC22061_2026_06_20_19_18_15_train	4.94 GB	4.96 GB

Usage Recommendations

This release is provided as training data. For local experiments, users may split by season to avoid mixing demonstrations from the same collection session across train and evaluation sets.

For policy learning, a typical setup is:

• Visual observations: one or more observation.images.* streams
• Proprioception: observation.state
• Optional tactile signal: observation.tactile
• Supervision target: action

For multi-view policies, start with:

observation.images.head_left
observation.images.head_right
observation.images.wrist_left
observation.images.wrist_right

Then add tactile video streams if your model can use high-resolution tactile observations:

observation.images.tactile_deform
observation.images.tactile_raw

Dataset Notes

• This repository contains only the LeRobot exports prepared for release.
• Raw POC recording folders and intermediate HDF5 folders are intentionally excluded from this release.
• The dataset is released by season, not as a single flattened LeRobot root.
• Every released season includes both lerobot3.0 and lerobotv2.1.
• motor_j0 to motor_j6 are the torso/motor-related dimensions in observation.state and action.
• The task is contact-rich and precision-sensitive: policies should expect object contacts, hand-part occlusions, tactile events, and fine pose adjustments.

License and Terms

This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). You may use, share, and adapt the dataset, including for commercial purposes, provided that you give appropriate attribution.

If you use the dataset for RoCo IROS 2026 Challenge participation, please also follow the official competition rules and evaluation protocol.

Citation

If this dataset contributes to your research, please cite or acknowledge the dataset.

@misc{roco_task_board_assembly_2026,
  title        = {RoCo Task Board Assembly LeRobot Dataset},
  howpublished = {\url{https://huggingface.co/datasets/SharpaIT/RoCo_TaskBoardAssembly}},
  year         = {2026}
}