We propose a point-cloud-based system for robots to predict a sequence of pick-and-place operations for transforming an initial object grasp pose to the desired object grasp poses. We introduce a new and challenging synthetic dataset for learning and evaluating the proposed approach. If you find this project useful for your research, please cite:

@inproceedings{
cheng2021learning,
title={Learning to Regrasp by Learning to Place},
author={Shuo Cheng and Kaichun Mo and Lin Shao},
booktitle={5th Annual Conference on Robot Learning },
year={2021},
url={https://openreview.net/forum?id=Qdb1ODTQTnL}
}

Real-world regrasping demo:

• python 3.8 (Anaconda)
• pip install -r requirements.txt

Visualization of sample stable poses:

Please download the dataset and place it inside this folder.

• Evaluating synthetic data: python scripts/evaluate_testset.py
• Evaluating real data: bash scripts/test_real_data.sh

• Please organize your data in the real_data folder as the example provided
• Please make your data as clean and complete as possible since an offset (x_mean, y_mean, z_min) will be subtracted for centralizing the point cloud

• Train generator: bash scripts/train_pose_generation.sh
• Train classifier: bash scripts/train_multi_task.sh