# Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic-Guided Grasp Generation **[Project Page](https://multi-graspllm.github.io/)** | **[arXiv](https://arxiv.org/abs/2412.08468)** --- ## Updates - **2025.3**: We add the Jaco hand data which is our final version. - **2025.1**: We add the Barrett hand data. - **2024.12**: Released the Multi-GraspSet dataset with meshes of objects. - **2024.12**: Released the Multi-GraspSet dataset with contact annotations. --- ## Overview We introduce **Multi-GraspSet**, the first large-scale multi-hand grasp dataset enriched with automatic contact annotations.
Structure of Multi-GraspLLM

The Construction process of Multi-GraspSet

Structure of Multi-GraspLLM

Visualization of Multi-GraspSet with contact annotations

--- ## Installation Follow these steps to set up the evaluation environment: 1. **Create the Environment** ```bash conda create --name eval python=3.9 ``` 2. **Install PyTorch and Dependencies** ```bash pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 ``` > ⚠️ Ensure the CUDA toolkit version matches your installed PyTorch version. 3. **Install Pytorch Kinematics** ```bash cd ./pytorch_kinematics pip install -e . ``` 4. **Install Remaining Requirements** ```bash pip install -r requirements_eval.txt ``` --- ## Visualize the Dataset 1. **Run the Visualization Code** Open and execute the `vis_mid_dataset.ipynb` file to visualize the dataset.