🦾 Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation A pre-trained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. Полный набор инструментов Python для обучения роботов на основе имитационного обучения и обучения роботов путем демонстрации. pip install rofunc import rofunc as rf import numpy as np from isaacgym import gymutil from importlib_resources import files # Demo raw_demo_l = np.load(files('rofunc.data.RAW_DEMO').joinpath('taichi_raw_l.npy')) raw_demo_r = np.load(files('rofunc.data.RAW_DEMO').joinpath('taichi_raw_r.npy')) demos_x_l = [raw_demo_l[300:435, :], raw_demo_l[435:570, :], raw_demo_l[570:705, :]] demos_x_r = [raw_demo_r[300:435, :], raw_demo_r[435:570, :], raw_demo_r[570:705, :]] rf.lqt.plot_3d_bi(demos_x_l, demos_x_r, ori=False, save=False) # TP-GMM show_demo_idx = 1 _, _, gmm_rep_l, gmm_rep_r = rf.tpgmm.bi(demos_x_l, demos_x_r, show_demo_idx=show_demo_idx, plot=True) 🖥 Github: https://github.com/skylark0924/rofunc 📕 Paper: https://arxiv.org/abs/2306.12677v1 🔗Dataset: https://paperswithcode.com/dataset/plasticinelab ai_machinelearning_big_data