Question about the camera

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. Later you can re-enter the lauched docker environment without re-compilation by:
```
docker exec -it catgrasp bash
```

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
```

	for _ in range(len(kd_indices)):
	if sqr_distances[_] != 0:
	normal = normals_for_sample[kd_indices[_]]
	normal = normal.reshape(-1, 1)
	if np.linalg.norm(normal) != 0:
	normal /= np.linalg.norm(normal)
	M += np.matmul(normal, normal.T)
	if sum(sum(M)) == 0:
	print("M matrix is empty as there is no point near the neighbour")
	self.params['r_ball'] *= 2
	print(f"Here is a bug, if points amount is too little it will keep trying and never go outside. Update r_ball to {self.params['r_ball']} and resample")
	return self.sample_one_surface_point(selected_surface,selected_normal,points_for_sample,normals_for_sample,background_pts,sphere_pts,seed)

	approach_normal = -selected_normal.reshape(3)
	approach_normal /= np.linalg.norm(approach_normal)
	eigval, eigvec = np.linalg.eig(M)
	def proj(u,v):
	u = u.reshape(-1)
	v = v.reshape(-1)
	return np.dot(u,v)/np.dot(u,u) * u
	minor_pc = eigvec[:, np.argmin(eigval)].reshape(3)
	minor_pc = minor_pc-proj(approach_normal,minor_pc)
	minor_pc /= np.linalg.norm(minor_pc)
	major_pc = np.cross(minor_pc, approach_normal)
	major_pc = major_pc / np.linalg.norm(major_pc)

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ICRA 2022 Presentation Video

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