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88237d1 56dea33 88237d1 c831788 88237d1 9c90a13 c831788 c3c02d6 6a6d007 c3c02d6 6a6d007 9c90a13 56dea33 88237d1 56dea33 88237d1 5e7215b 26866d7 88237d1 26866d7 88237d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | # 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)**
---
## Overview
We introduce **Multi-GraspSet**, the first large-scale multi-hand grasp dataset enriched with automatic contact annotations.
<table style="margin: auto; text-align: center;">
<tr>
<td>
<img src="./assets/picture/dataset_construction.png" alt="Structure of Multi-GraspLLM" width="1000">
</td>
</tr>
<tr>
<td>
<p>The Construction process of Multi-GraspSet</p>
</td>
</tr>
</table>
<table style="margin: auto; text-align: center;">
<tr>
<td>
<img src="./assets/picture/vis_dataset_grasp_output_01.png" alt="Structure of Multi-GraspLLM" width="600">
</td>
</tr>
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<td>
<p>Visualization of Multi-GraspSet with contact annotations</p>
</td>
</tr>
</table>
---
## Updates
- **2024.12**: Released the Multi-GraspSet dataset with meshes of objects.
- **2024.12**: Released the Multi-GraspSet dataset 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.
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