You can deploy this model and start earning money today!

kevinywu/
hug

Name: hug
Availability: InStock

robotics

grasping

learning-from-humans

dexterous-manipulation

2606.17054

mit

Views

No views yet

Human Universal Grasping (HUG)

HUG is a flow-matching model that generates diverse human grasps for any user-specified object in a single RGB-D image. By learning from a large-scale egocentric dataset of human grasps (1M-HUGs), the model can predict human-like grasps that can be retargeted to various robot hands for zero-shot manipulation.

📄 Paper: Human Universal Grasping
🌐 Website: grasping.io
💻 Code: github.com/kevinywu/hug

Installation

The codebase is tested on Ubuntu 22.04/24.04, CUDA 12.8, PyTorch 2.9.1, and Python 3.10.

bash

# 1) Environment setup
conda env create -f environment.yaml && conda activate hug
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu128
pip install torch-cluster -f https://data.pyg.org/whl/torch-2.9.1+cu128.html
pip install --no-build-isolation git+https://github.com/mattloper/chumpy.git@580566e
pip install -e .

Please refer to the official repository for instructions on downloading required assets like MANO models.

Usage

Download Weights

Download the full model weights (.safetensors) using the huggingface-cli:

hf download kevinywu/hug hug_full.safetensors --local-dir checkpoints/

Inference

HUG predicts human grasps in MANO form. You can run the interactive application to predict grasps for objects in the camera frame:

bash

CKPT=checkpoints/hug_full.safetensors
DATA=data/hug_bench/

# Launch the app: click an object to predict a grasp
python -m hug.app --checkpoint-path "$CKPT" --dataset-path "$DATA" --save-pred

# Visualize saved predictions
python -m hug.visualize_predictions --dataset-path "$DATA"

Citation

bibtex

@article{wu2026hug,
  title={Human Universal Grasping},
  author={Kevin Yuanbo Wu and Tianxing Zhou and Isaac Tu and Billy Yan and Irmak Guzey and David Fouhey and Dandan Shan and Lerrel Pinto},
  journal={arXiv preprint arXiv:2606.17054},
  year={2026}
}