Add robotics pipeline tag and improve model card

README.md

@@ -1,31 +1,60 @@
 ---
 license: mit
+pipeline_tag: robotics
 tags:
-  - robotics
-  - grasping
-  - learning-from-humans
-  - dexterous-manipulation
+- robotics
+- grasping
+- learning-from-humans
+- dexterous-manipulation
 ---
 
 # Human Universal Grasping (HUG)
 
-Learning dexterous multifingered grasping entirely from human data.
+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**: [arXiv:2606.17054](https://arxiv.org/abs/2606.17054)
+- 📄 **Paper**: [Human Universal Grasping](https://arxiv.org/abs/2606.17054)
 - 🌐 **Website**: [grasping.io](https://grasping.io)
 - 💻 **Code**: [github.com/kevinywu/hug](https://github.com/kevinywu/hug)
 
-## Files
+## Installation
 
-- `hug_full.safetensors` — full model weights (EMA, bf16).
+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](https://github.com/kevinywu/hug) for instructions on downloading required assets like MANO models.
 
 ## Usage
 
+### Download Weights
+
+Download the full model weights (`.safetensors`) using the `huggingface-cli`:
+
 ```bash
 hf download kevinywu/hug hug_full.safetensors --local-dir checkpoints/
 ```
 
-See the [code repo](https://github.com/kevinywu/hug) for inference instructions.
+### 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
 
@@ -36,4 +65,4 @@ See the [code repo](https://github.com/kevinywu/hug) for inference instructions.
   journal={arXiv preprint arXiv:2606.17054},
   year={2026}
 }
-```
+```