GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF (ICRA 2023)

This is the official repository of GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF.

For more information, please visit our project page.

Introduction

In this work, we propose a multiview RGB-based 6-DoF grasp detection network, GraspNeRF, that leverages the generalizable neural radiance field (NeRF) to achieve material-agnostic object grasping in clutter. Compared to the existing NeRF-based 3-DoF grasp detection methods that rely on densely captured input images and time-consuming per-scene optimization, our system can perform zero-shot NeRF construction with sparse RGB inputs and reliably detect 6-DoF grasps, both in real-time. The proposed framework jointly learns generalizable NeRF and grasp detection in an end-to-end manner, optimizing the scene representation construction for the grasping. For training data, we generate a large-scale photorealistic domain-randomized synthetic dataset of grasping in cluttered tabletop scenes that enables direct transfer to the real world. Experiments in synthetic and real-world environments demonstrate that our method significantly outperforms all the baselines in all the experiments.

Overview

This repository provides:

PyTorch code, and weights of GraspNeRF.
Grasp Simulator based on blender and pybullet.
Multiview 6-DoF Grasping Dataset Generator and Examples.

Dependency

Please run

pip install -r requirements.txt

to install dependency.

(optional) Please install blender 2.93.3--Ubuntu if you need simulation.

Data & Checkpoints

Please generate or download and uncompress the example data to data/ for training, and rendering assets to data/assets for simulation. Specifically, download imagenet valset to data/assets/imagenet/images/val which is used as random texture in simulation.
We provide pretrained weights for testing. Please download the checkpoint to src/nr/ckpt/test.

Testing

Our grasp simulation pipeline is depend on blender and pybullet. Please verify the installation before running simulation.

After the dependency and assets are ready, please run

bash run_simgrasp.sh

Training

After the training data is ready, please run

bash train.sh GPU_ID

e.g. bash train.sh 0.

Data Generator

Download the scene descriptor files from GIGA and assets.
For example, run

bash run_pile_rand.sh

in ./data_generator for pile data generation.

Citation

If you find our work useful in your research, please consider citing:

@article{Dai2023GraspNeRF,
  title={GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF},
  author={Qiyu Dai and Yan Zhu and Yiran Geng and Ciyu Ruan and Jiazhao Zhang and He Wang},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2023}

License

This work and the dataset are licensed under CC BY-NC 4.0.

Contact

If you have any questions, please open a github issue or contact us:

Qiyu Dai: qiyudai@pku.edu.cn, Yan Zhu: zhuyan_@stu.pku.edu.cn, He Wang: hewang@pku.edu.cn

PKU-EPIC/GraspNeRF