.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/learning_ml/example_taichi.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_learning_ml_example_taichi.py: Taichi ====== This example shows how to use the TPGMM and TPGMMBi classes to learn Taichi pushing hand on a humanoid robot./ .. GENERATED FROM PYTHON SOURCE LINES 7-109 .. code-block:: default import numpy as np import rofunc as rf # Parameters task_param = { "nbData": 500, # Number of data points "nbPoints": 3, # Number of viapoints "nbVarPos": 7, # Dimension of position data "nbDeriv": 2, # Number of static and dynamic features (2 -> [x,dx]) "dt": 1e-2, # Time step duration "rfactor": 1e-8 # Control cost } task_param["nb_var"] = task_param["nbVarPos"] * task_param["nbDeriv"] def uni(): raw_demo = np.load('/home/ubuntu/Downloads/OneDrive_2023-03-10/010-003/LeftHand.npy') raw_demo = np.expand_dims(raw_demo, axis=0) demos_x = np.vstack((raw_demo[:, 430:525, :], raw_demo[:, 240:335, :], raw_demo[:, 335:430, :])) # Reproductions for the same situations representation = rf.ml.tpgmm.TPGMM(demos_x) model = representation.fit(plot=True) # Reproductions for new situations ref_demo_idx = 2 A, b = representation.demos_A_xdx[ref_demo_idx][0], representation.demos_b_xdx[ref_demo_idx][0] b[1] = b[0] task_params = {'A': A, 'b': b} traj = representation.generate(model, ref_demo_idx, task_params, plot=True) via_points = traj[:, :7] filter_indices = [i for i in range(0, len(via_points), 5)] + [0] via_points = via_points[filter_indices] controller = rf.planning.lqt.LQT(via_points) u_hat, x_hat, mu, idx_slices = controller.solve() rf.lqt.plot_3d_uni(x_hat, mu, idx_slices, ori=False, save=True, save_file_name='v_right.npy') def bi(): # raw_demo = np.load('/home/ubuntu/Downloads/OneDrive_2023-03-10/010-004/LeftHand.npy') raw_demo = np.load('/home/ubuntu/Data/20230704/xsens data_mvnx/010-010/segment/14_LeftHand.npy') raw_demo = np.expand_dims(raw_demo, axis=0) # demos_left_x = np.vstack((raw_demo[:, 134:254, :], raw_demo[:, 250:370, :], raw_demo[:, 370:490, :])) demos_left_x = np.vstack((raw_demo[:, 500:635, :], raw_demo[:, 635:770, :], raw_demo[:, 770:905, :])) # raw_demo = np.load('/home/ubuntu/Downloads/OneDrive_2023-03-10/010-004/RightHand.npy') raw_demo = np.load('/home/ubuntu/Data/20230704/xsens data_mvnx/010-010/segment/10_RightHand.npy') raw_demo = np.expand_dims(raw_demo, axis=0) # demos_right_x = np.vstack((raw_demo[:, 134:254, :], raw_demo[:, 250:370, :], raw_demo[:, 370:490, :])) demos_right_x = np.vstack((raw_demo[:, 500:635, :], raw_demo[:, 635:770, :], raw_demo[:, 770:905, :])) # Define the task parameters start_xdx_l = [demos_left_x[i][0] for i in range(len(demos_left_x))] # TODO: change to xdx end_xdx_l = [demos_left_x[i][-1] for i in range(len(demos_left_x))] start_xdx_r = [demos_right_x[i][0] for i in range(len(demos_right_x))] end_xdx_r = [demos_right_x[i][-1] for i in range(len(demos_right_x))] task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']}, 'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}} representation = rf.ml.tpgmm.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=True) model_l, model_r = representation.fit() # # Reproductions for the same situations # traj_l, traj_r = representation.reproduce(model_l, model_r, show_demo_idx=2, plot=True) # Reproductions for new situations ref_demo_idx = 2 A_l, b_l = representation.repr_l.demos_A_xdx[ref_demo_idx][0], representation.repr_l.demos_b_xdx[ref_demo_idx][0] b_l[1] = b_l[0] A_r, b_r = representation.repr_r.demos_A_xdx[ref_demo_idx][0], representation.repr_r.demos_b_xdx[ref_demo_idx][0] b_r[1] = b_r[0] task_params = {'Left': {'A': A_l, 'b': b_l}, 'Right': {'A': A_r, 'b': b_r}} traj_l, traj_r = representation.generate(model_l, model_r, ref_demo_idx, task_params, plot=True) via_points = traj_l[:, :7] filter_indices = [i for i in range(0, len(via_points) - 5, 5)] + [0] via_points = via_points[filter_indices] controller = rf.planning.lqt.LQT(via_points) u_hat, x_hat_l, mu_l, idx_slices = controller.solve() via_points = traj_r[:, :7] filter_indices = [i for i in range(0, len(via_points) - 5, 5)] + [0] via_points = via_points[filter_indices] controller = rf.planning.lqt.LQT(via_points) u_hat, x_hat_r, mu_r, idx_slices = controller.solve() rf.lqt.plot_3d_bi(x_hat_l, x_hat_r, mu_l, mu_r, idx_slices, ori=False, save=True, save_file_name=['h_left.npy', 'h_right.npy']) def export(): # mvnx_file = '/home/ubuntu/Downloads/OneDrive_2023-03-10/010-004.mvnx' mvnx_file = '/home/ubuntu/Data/20230704/xsens data_mvnx/010-010.mvnx' rf.xsens.export(mvnx_file) if __name__ == '__main__': bi() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.000 seconds) .. _sphx_glr_download_examples_learning_ml_example_taichi.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: example_taichi.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: example_taichi.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_