if __name__ == '__main__': import pylab as plt from plot_tools import plot_pos_vel_acc_trajectory # only Transformation system (f=0) dmp = DiscreteDMP() dmp.setup(1.1, 2.2, 1.0) traj = [] for x in range(1000): #if x == 500: # dmp.goal = 4.0 dmp.run_step() traj.append([dmp.x, dmp.xd, dmp.xdd]) fig = plt.figure('f=0 (transformation system only)', figsize=(10, 3)) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) plot_pos_vel_acc_trajectory((ax1, ax2, ax3), traj, dmp.delta_t, label='DMP $f=0$', linewidth=1) fig.tight_layout() plt.show()
def main(): # start and goal of the movement (1-dim) start = 0.5 goal = 1.0 ####### generate a trajectory (minimum jerk) # duration duration = 1.0 # time steps delta_t = 0.001 # trajectory is a list of 3-tuples with (pos,vel,acc) traj = min_jerk_traj(start, goal, 1.0, delta_t) traj_freq = int(1.0 / delta_t) ####### learn DMP dmp = DiscreteDMP( False, LWR(activation=0.3, exponentially_spaced=False, n_rfs=8, use_offset=True)) dmp.learn_batch(traj, traj_freq) ####### learn DMP # setup DMP with start and goal dmp.setup(start + 0.4, goal + 0.2, duration) # trajectory reproduced_traj = [] # states of canonical system (plotting) s = [] s_time = [] # run steps (for each point of the sample trajectory) for _ in xrange(int(dmp.tau / dmp.delta_t)): # change goal while execution #if x == 500: # dmp.goal = 4.0 # run a single integration step dmp.run_step() # remember canonical system values s.append(dmp.s) s_time.append(dmp.s_time) # save reproduced trajectory reproduced_traj.append([dmp.x, dmp.xd, dmp.xdd]) ####### PLOTTING fig_pos = plt.figure('dmp batch learn from min jerk', figsize=(7, 5)) ax_pos2 = fig_pos.add_subplot(111) plot_time = np.arange(len(traj)) * delta_t ax_pos2.plot(plot_time, np.asarray(traj)[:, 0], label='demonstration') ax_pos2.plot(plot_time, np.asarray(reproduced_traj)[:, 0], label='adapted reproduction', linestyle='dashed') ax_pos2.legend(loc='upper left') plt.show() # create figure fig = plt.figure('dmp batch learn from min jerk', figsize=(16, 4.6)) # create axes ax_pos = fig.add_subplot(131) ax_vel = fig.add_subplot(132) ax_acc = fig.add_subplot(133) # plot on the axes plot_pos_vel_acc_trajectory((ax_pos, ax_vel, ax_acc), traj, delta_t, label='demonstration') plot_pos_vel_acc_trajectory((ax_pos, ax_vel, ax_acc), reproduced_traj, dmp.delta_t, label='reproduction', linestyle='dashed') fig.tight_layout() plt.show() fig = plt.figure('ftarget', figsize=(16, 4.6)) # plot ftarget (real and predicted) ax_ft = fig.add_subplot(131) ax_ft.plot(dmp.target_function_input, dmp.target_function_ouput, linewidth=2, label=r'$f_{target}(s)$') ax_ft.plot(dmp.target_function_input, dmp.target_function_predicted, '--', linewidth=2, label=r'$f_{predicted}(s)$') #dashes=(3, 3) ax_ft.set_xlabel(r'$s$') ax_ft.set_ylabel(r'$f(s)$') ax_ft.legend() # kernels (lwr) ax_kernel = fig.add_subplot(132) dmp.lwr_model.plot_kernels(ax_kernel) ax_kernel.set_xlabel(r'$s$') ax_kernel.set_ylabel(r'$\psi(s)$') # canonical system # ax_cs = fig.add_subplot(236) # ax_cs.plot(s_time, s) # ax_cs.set_xlabel('Time [s]') # ax_cs.set_ylabel(r'$s$') # weights of kernels (w) ax_w = fig.add_subplot(133) ax_w.set_xlabel(r'$s$') ax_w.set_ylabel(r'$f(s)$') dmp.lwr_model.plot_linear_models(ax_w) # lwr_wights = dmp.lwr_model.get_thetas() # ax_w.bar(range(len(lwr_wights)), lwr_wights, width=0.3) # ax_w.set_xlabel('$w_i$') # ax_w.set_ylabel('') fig.tight_layout() plt.show()
def main(): # start and goal of the movement (1-dim) start = 0.5 goal = 1.0 ####### generate a trajectory (minimum jerk) # duration duration = 1.0 # time steps delta_t = 0.001 # trajectory is a list of 3-tuples with (pos,vel,acc) traj = min_jerk_traj(start, goal, 1.0, delta_t) traj_freq = int(1.0 / delta_t) ####### learn DMP dmp = DiscreteDMP(False, LWR(activation=0.3, exponentially_spaced=False, n_rfs=8, use_offset=True)) dmp.learn_batch(traj, traj_freq) ####### learn DMP # setup DMP with start and goal dmp.setup(start+0.4, goal+0.2, duration) # trajectory reproduced_traj = [] # states of canonical system (plotting) s = [] s_time = [] # run steps (for each point of the sample trajectory) for _ in xrange(int(dmp.tau / dmp.delta_t)): # change goal while execution #if x == 500: # dmp.goal = 4.0 # run a single integration step dmp.run_step() # remember canonical system values s.append(dmp.s) s_time.append(dmp.s_time) # save reproduced trajectory reproduced_traj.append([dmp.x, dmp.xd, dmp.xdd]) ####### PLOTTING fig_pos = plt.figure('dmp batch learn from min jerk', figsize=(7, 5)) ax_pos2 = fig_pos.add_subplot(111) plot_time = np.arange(len(traj)) * delta_t ax_pos2.plot(plot_time, np.asarray(traj)[:, 0], label='demonstration') ax_pos2.plot(plot_time, np.asarray(reproduced_traj)[:, 0], label='adapted reproduction', linestyle='dashed') ax_pos2.legend(loc='upper left') plt.show() # create figure fig = plt.figure('dmp batch learn from min jerk', figsize=(16, 4.6)) # create axes ax_pos = fig.add_subplot(131) ax_vel = fig.add_subplot(132) ax_acc = fig.add_subplot(133) # plot on the axes plot_pos_vel_acc_trajectory((ax_pos, ax_vel, ax_acc), traj, delta_t, label='demonstration') plot_pos_vel_acc_trajectory((ax_pos, ax_vel, ax_acc), reproduced_traj, dmp.delta_t, label='reproduction', linestyle='dashed') fig.tight_layout() plt.show() fig = plt.figure('ftarget', figsize=(16, 4.6)) # plot ftarget (real and predicted) ax_ft = fig.add_subplot(131) ax_ft.plot(dmp.target_function_input, dmp.target_function_ouput, linewidth=2, label=r'$f_{target}(s)$') ax_ft.plot(dmp.target_function_input, dmp.target_function_predicted, '--', linewidth=2, label=r'$f_{predicted}(s)$') #dashes=(3, 3) ax_ft.set_xlabel(r'$s$') ax_ft.set_ylabel(r'$f(s)$') ax_ft.legend() # kernels (lwr) ax_kernel = fig.add_subplot(132) dmp.lwr_model.plot_kernels(ax_kernel) ax_kernel.set_xlabel(r'$s$') ax_kernel.set_ylabel(r'$\psi(s)$') # canonical system # ax_cs = fig.add_subplot(236) # ax_cs.plot(s_time, s) # ax_cs.set_xlabel('Time [s]') # ax_cs.set_ylabel(r'$s$') # weights of kernels (w) ax_w = fig.add_subplot(133) ax_w.set_xlabel(r'$s$') ax_w.set_ylabel(r'$f(s)$') dmp.lwr_model.plot_linear_models(ax_w) # lwr_wights = dmp.lwr_model.get_thetas() # ax_w.bar(range(len(lwr_wights)), lwr_wights, width=0.3) # ax_w.set_xlabel('$w_i$') # ax_w.set_ylabel('') fig.tight_layout() plt.show()