Datasets:

DEXROBOT
/

DexCanvas

license: odbl
task_categories:
  - robotics
  - video-classification
  - image-classification
  - object-detection
tags:
  - dexterous-manipulation
  - hand-object-interaction
  - motion-capture
  - physics-simulation
  - rgbd
  - contact-forces
  - computer-vision
size_categories:
  - 10K<n<100K

DexCanvas: Dexterous Manipulation Dataset v0.1

⚠️ TEST RELEASE: This is a preview version containing 1% of the full dataset. Contact force data is not included in v0.1.

DexCanvas is a large-scale hybrid dataset for robotic hand-object interaction research, combining real human demonstrations with physics-validated simulation data.

Dataset Statistics (v0.1 Test Release)

Total Frames: ~30 million multi-view RGB-D frames
Total Duration: ~70 hours of dexterous hand-object interactions
Real Demonstrations: ~0.7 hours of human mocap data (1/100 of collected data)
Expansion Ratio: 100× from real to simulated data
Manipulation Types: 21 types based on Cutkosky taxonomy
Objects: 30 objects (geometric primitives + YCB objects)
Capture Rate: 100 Hz optical motion capture

Manipulation Coverage

The dataset spans four primary grasp categories:

Power Grasps: Full-hand wrapping grips
Intermediate Grasps: Mixed precision-power combinations
Precision Grasps: Fingertip-based manipulation
In-Hand Manipulation: Object reorientation and repositioning

All 21 manipulation types follow the Cutkosky grasp taxonomy.

Data Modalities

Each frame includes:

RGB-D Data: Multi-view color and depth images
Hand Pose: MANO hand parameters with high-precision tracking
Object State: 6-DoF pose and object wrenches
Annotations: Per-frame labels and metadata

Note: Contact force data is not included in v0.1. Contact forces will be available in future releases.

Data Pipeline

The dataset is generated through three stages:

Real Capture: Optical motion capture of human demonstrations at 30 Hz
Force Reconstruction: RL-based physics simulation to infer contact forces
Physics Validation: Verification of contact points, forces, and object dynamics

This hybrid approach provides contact information impossible to observe directly in real-world scenarios while maintaining physical accuracy.

Installation

pip install datasets huggingface_hub

For image processing and visualization:

pip install pillow numpy torch

Authenticate with HuggingFace (required for private datasets):

huggingface-cli login

Or set your token as an environment variable:

export HF_TOKEN="your_token_here"

Quick Start

Data Structure

{
  "trajectory_meta_data": {
    "generated_data": "int",
    "data_fps": "int",
    "mocap_raw_data_source": {
      "operator": "str",
      "object": "str",
      "gesture": "str"
    },
    "total_frames": "int",
    "mano_hand_shape": "(10,)"
    //...
  },
  "sequence_info": {
    "timestamp": "(T,)",
    "hand_joint": {
      "position": "(T, 3)",
      "rotation": "(T, 3)",
      "finger_pose": "(T, 48)"
    },
    "object_info": {
      "pose": "(T, 6)"
    },
    "mano_model_output": {
      "joints": "(T, 63)"
    }
  }
}

Visualization

Visualize trajectories using the mocap_loader:

# Install dependencies
pip install open3d trimesh scipy

# Visualize trajectory
python -m hand_trajectory_loader.examples.visualize_trajectory \
    dataset.parquet 0 \
    --mano-model assets/mano/models/MANO_RIGHT.pkl \
    --object assets/objects/cube1.stl \
    --show-joints

Controls: SPACE pause/resume, M toggle hand mesh, O toggle object, Q quit

Version Information

v0.1 (Test Release) includes:

1% of collected real human demonstration data
MANO hand parameters
Object pose data
Manipulation type annotations

Coming in future releases:

Complete dataset (100× larger than v0.1)
Contact force data with physics validation
Additional objects and manipulation types
Extended annotations and metadata

Contact

Research Collaboration Academic inquiries: lyw@dex-robot.com

Business Inquiries Business collaboration: info@dex-robot.com

Website https://www.dex-robot.com/en https://dexcanvas.github.io/

Citation

@article{dexcanvas2025,
  title={DexCanvas: A Large-Scale Hybrid Dataset for Dexterous Manipulation},
  author={DexRobot Team},
  year={2025},
  eprint={2510.15786},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2510.15786}
}

License

This dataset is released under the Open Database License (ODbL).

Developed by DexRobot Team Last Updated: October 2025