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Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, Yuke Zhu

RSS (Robotics: Science and Systems) 2021

Project | arxiv

Introduction

GIGA (Grasp detection via Implicit Geometry and Affordance) is a network that jointly detects 6 DOF grasp poses and reconstruct the 3D scene. GIGA takes advantage of deep implicit functions, a continuous and memory-efficient representation, to enable differentiable training of both tasks. GIGA takes as input a Truncated Signed Distance Function (TSDF) representation of the scene, and predicts local implicit functions for grasp affordance and 3D occupancy. By querying the affordance implict functions with grasp center candidates, we can get grasp quality, grasp orientation and gripper width at these centers. GIGA is trained on a synthetic grasping dataset generated with physics simulation.

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

Installation

1. Create a conda environment.

2. Install packages list in requirements.txt. Then install torch-scatter following here, based on pytorch version and cuda version.

3. Go to the root directory and install the project locally using pip

pip install -e .

4. Build ConvONets dependents by running python scripts/convonet_setup.py build_ext --inplace.

5. Download the data, then unzip and place the data folder under the repo's root. Pretrained models of GIGA, GIGA-Aff and VGN are in data/models.

Self-supervised Data Generation

Raw synthetic grasping trials

Pile scenario:

python scripts/generate_data_parallel.py --scene pile --object-set pile/train --num-grasps 4000000 --num-proc 40 --save-scene ./data/pile/data_pile_train_random_raw_4M

Packed scenario:

python scripts/generate_data_parallel.py --scene packed --object-set packed/train --num-grasps 4000000 --num-proc 40 --save-scene ./data/pile/data_packed_train_random_raw_4M

Please run python scripts/generate_data_parallel.py -h to print all options.

Data clean and processing

First clean and balance the data using:

python scripts/clean_balance_data.py /path/to/raw/data

Then construct the dataset (add noise):

python scripts/construct_dataset_parallel.py --num-proc 40 --single-view --add-noise dex /path/to/raw/data /path/to/new/data

Save occupancy data

Sampling occupancy data on the fly can be very slow and block the training, so I sample and store the occupancy data in files beforehand:

python scripts/save_occ_data_parallel.py /path/to/raw/data 100000 2 --num-proc 40

Please run python scripts/save_occ_data_parallel.py -h to print all options.

Training

Train GIGA

Run:

# GIGA
python scripts/train_giga.py --dataset /path/to/new/data --dataset_raw /path/to/raw/data

Simulated grasping

Run:

python scripts/sim_grasp_multiple.py --num-view 1 --object-set (packed/test | pile/test) --scene （packed ｜ pile) --num-rounds 100 --sideview --add-noise dex --force --best --model /path/to/model --type (vgn | giga | giga_aff) --result-path /path/to/result

This commands will run experiment with each seed specified in the arguments.

Run python scripts/sim_grasp_multiple.py -h to print a complete list of optional arguments.

Pre-trained models and pre-generated data

Pre-trained models

Pretrained models are also in the data.zip. They are in data/models.

Pre-generated data

As mentioned in the issue, data generation is very costly. So we upload the generated data. Because the occupancy data takes too much space (over 100G), we do not upload the occupancy data, you can generate them following the instruction in this section. This generation won't take too long time.

You can try wget --no-check-certificate $wget_link for direct download.

Related Repositories

1. Our code is largely based on VGN

2. We use ConvONets as our backbone.

Citing

@article{jiang2021synergies,
 author = {Jiang, Zhenyu and Zhu, Yifeng and Svetlik, Maxwell and Fang, Kuan and Zhu, Yuke},
 journal = {Robotics: science and systems},
 title = {Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations},
 year = {2021}
}