This is the official implementation of our paper:

Bowen Wen, Wenzhao Lian, Kostas Bekris, and Stefan Schaal. "CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation." IEEE International Conference on Robotics and Automation (ICRA) 2022.

Abstract

Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard to define and annotate. There is also yet no consensus on proper representations for modeling or off-the-shelf tools for performing task-relevant grasps. This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation. To achieve this, the entire framework is trained solely in simulation, including supervised training with synthetic label generation and self-supervised, hand-object interaction. In the context of this framework, this paper proposes a novel, object-centric canonical representation at the category level, which allows establishing dense correspondence across object instances and transferring task-relevant grasps to novel instances. Extensive experiments on task-relevant grasping of densely-cluttered industrial objects are conducted in both simulation and real-world setups, demonstrating the effectiveness of the proposed framework.

Bibtex

@article{wen2021catgrasp,
  title={CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation},
  author={Wen, Bowen and Lian, Wenzhao and Bekris, Kostas and Schaal, Stefan},
  journal={ICRA 2022},
  year={2022}
}

Supplementary Video

Click to watch

ICRA 2022 Presentation Video

Quick Setup

We provide docker environment and setup is as easy as below a few lines.

• If you haven't installed docker, firstly install (https://docs.docker.com/get-docker/).

• Run

  docker pull wenbowen123/catgrasp:latest
  

• To enter the docker, run below

  cd  docker && bash run_container.sh
  cd /home/catgrasp && bash build.sh
  

  Now the environment is ready to run training or testing.

Data

• Download object models and pretrained network weights from here. Then extract and replace the files in this repo, to be like:

  catgrasp
  ├── artifacts
  ├── data
  └── urdf

Testing

python run_grasp_simulation.py

You should see the demo starting like below. You can play with the settings in config_run.yml, including changing different object instances within the category while using the same framework

Training

In the following, we take the nut category as an example to walk through

• Compute signed distance function for all objects of the category

  python make_sdf.py --class_name nut
  

• Pre-compute offline grasps of training objects. This generates and evaluates grasp qualities regardless of their task-relevance. To visualize and debug the grasp quality evaluation change to --debug 1

  python generate_grasp.py --class_name nut --debug 0
  

• Self-supervised task-relevance discovery in simulation

  python pybullet_env/env_semantic_grasp.py --class_name nut --debug 0
  

  Changing --debug 0 to --debug 1, you are able to debug and visualize the process

  

  The affordance results will be saved in data/object_models. The heatmap file XXX_affordance_vis can be visualized as in the below image, where warmer area means higher task-relevant grasping region P(T|G)

  

• Make the canonical model that stores category-level knowledge

  python make_canonical.py --class_name nut
  

  

• Training data generation of piles

  python generate_pile_data.py --class_name nut
  

  

• Process training data, including generating ground-truth labels

  python tool.py
  

• To train NUNOCS net, examine the settings in config_nunocs.yml, then

  python train_nunocs.py
  

• To train grasping-Q net, examine the settings in config_grasp.yml, then

  python train_grasp.py
  

• To train instance segmentation net, examine the settings in PointGroup/config/config_pointgroup.yaml, then

  python train_pointgroup.py