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license: cc-by-4.0
language:
- en
pipeline_tag: robotics
tags:
- Grasping
- Manipulation
- IA
- CVAE
---
# **GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation**
[Julien Mérand](https://julienmerand.github.io/portfolio/)<sup>1</sup>, [Boris Meden](https://scholar.google.com/citations?user=knXPf8oAAAAJ&hl=fr)<sup>1</sup>, [Mathieu Grossard](mailto:mathieu.grossard@cea.fr)<sup>1</sup>, [Liming Chen](https://scholar.google.com/citations?user=VOPW5YYAAAAJ&hl=fr)<sup>2</sup>
<sup>1</sup>Université Paris-Saclay, CEA-List <sup>2</sup>École Centrale de Lyon, CNRS, LIRIS, UMR5205, Institut Universitaire de France (IUF)
## 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
[**Project Page**](https://cea-list.github.io/goagweb/) | [**arXiv**](https://arxiv.org/abs/2608.19759)
**GOAG** is novel deep generative model that learns a compact latent representation of a specific gripper's contact surface distribution, enabling the efficient sampling of valid grasp configurations without relying on object-specific training data.
By introducing object features only at inference time, GOAG can effectively retrieve admissible contact areas that are compatible with the gripper’s capabilities.
See [https://github.com/CEA-LIST/GOAG](https://github.com/CEA-LIST/GOAG) for full code and instructions.
Your directory tree should strictly follow this structure:
```bash
# Model Checkpoints
GOAG # Main folder
├── ...
├── logs # ckpts folder
├── allegro_cvae
├── allegro_pointnet
├── barrett_cvae
├── barrett_pointnet
├── shadowhand_cvae
└── shadowhand_pointnet
└── ...
# Dataset Directory
GOAG_DATA
├── handprints
├── pointclouds
├── dexgrab
├── dexgraspnet
├── multidex
├── realdex
└── unidexgrasp
├── urdf
├── objects
│ ├── dexgrab
│ ├── dexgraspnet
│ ├── multidex
│ ├── realdex
│ └── unidexgrasp
└── robot
├── allegro
├── barrett
└── shadowhand
└── workspaces
``` |