Training and simulation data for GP-net+

Training and simulation data for GP-net+. For simulation data, download "gpnetplus_simulation_data.zip" and unpack it into the GP-net+ directory. To improve handling of the training dataset (total size 25GB+), we split the dataset into several .zip files, named val.zip (validation data) and train_[0-6].zip (training data). Download all files individually and extract them into a single folder. Combine all files train_[0-6].zip directory into a single directory called 'train', for example by using the 'move_train_data.sh' script provided. The final structure for the dataset should look similar to this: gpnet_data |-- val |-- depth_image_0000000.npz |-- depth_image_0000001.npz ... |--segmask_image_0052346.npz |-- train |-- depth_image_0000000.npz |-- depth_image_0000001.npz ... |-- segmask_image_0602506.npz |-- segmask_image_0602507.npz For generation of the training and simulation data, the following mesh databases have been used: B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar,"Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set," IEEE Robotics and Automation Magazine, vol. 22, no. 3, pp. 36–52, 2015 A. Singh, J. Sha, K. S. Narayan, T. Achim, and P. Abbeel, "BigBIRD: A large-scale 3D database of object instances," 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 509–516, 2014. A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, "ShapeNet: An Information-Rich 3D Model Repository," Tech. Rep. arXiv:1512.03012 [cs.GR], Stanford University — Princeton University — Toyota Technological Institute at Chicago, 2015. D. Morrison, P. Corke, and J. Leitner, "EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4368–4375, 2020