Implicit Grasp Diffusion: Bridging the Gap between Dense Prediction and Sampling-based Grasping

Accepted by Conference on Robot Learning 2024 (CoRL 2024)

Introduction

This paper aims to bridge the gap between dense prediction and sampling-based methods. We propose a novel framework named Implicit Grasp Diffusion (IGD) that leverages implicit neural representations to extract expressive local features, and that generates grasps by sampling from diffusion models conditioned on these local features. We evaluated our model on a clutter removal task in both simulated and real-world environments. The experimental results have demonstrated the high grasp accuracy, strong noise robustness, and multi-modal grasp modeling of the proposed method. The code is open-source and a GitHub link will be provided in the final version of the paper.

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. We use the same data as GIGA. You can download the data, then unzip and place the data folder under the repo's root.

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 IGD

Run:

python scripts/train_igd.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 igd --result-path /path/to/result

This command will run the 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-generated data

Data generation is very costly. So it'd be better to use pre-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.

Scenario	Raw data	Processed data
Pile	link	link
Packed	link	link

Related Repositories

1. Our code is largely based on VGN and GIGA.

2. We use ConvONets as our backbone.

Citing

@inproceedings{song2024b,
  author = {Song, Pinhao and Li, Pengteng and Detry, Renaud},
  title = {Implicit Grasp Diffusion: Bridging the Gap between Dense Prediction and Sampling-based Grasping},
  year = {2024},
  booktitle = {Conference on Robot Learning},
}