--- 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/)1,    [Boris Meden](https://scholar.google.com/citations?user=knXPf8oAAAAJ&hl=fr)1,    [Mathieu Grossard](mailto:mathieu.grossard@cea.fr)1,    [Liming Chen](https://scholar.google.com/citations?user=VOPW5YYAAAAJ&hl=fr)2 1Université Paris-Saclay, CEA-List    2École 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 ```