def main(train_index):
# load the data from pkl
datasets_norm_preproc = joblib.load(datasets_norm_preproc_path)
min_max_scaler = joblib.load(min_max_scaler_path)
noise_cov = joblib.load(noise_cov_path)
# create iProMPs sets
ipromps_set = [
ipromps_lib.IProMP(num_joints=num_joints,
num_obs_joints=num_obs_joints,
num_basis=num_basis,
sigma_basis=sigma_basis,
num_samples=len_norm,
sigmay=noise_cov,
min_max_scaler=min_max_scaler,
num_alpha_candidate=num_alpha_candidate)
for x in datasets_norm_preproc
]
train_set = []
for task_idx, demo_list in enumerate([train_index] *
len(datasets_norm_preproc)):
data = [datasets_norm_preproc[task_idx][i] for i in demo_list]
train_set.append(data)
# add demo for each IProMPs
for idx, ipromp in enumerate(ipromps_set):
for demo_idx in train_set[idx]:
demo_temp = np.hstack(
[demo_idx['left_hand'], demo_idx['left_joints']])
ipromp.add_demonstration(demo_temp) # spatial variance demo
ipromp.add_alpha(demo_idx['alpha']) # temporal variance demo
return ipromps_set
def main():
# load the data from pkl
task_name = joblib.load(task_name_path)
datasets_norm_preproc = joblib.load(datasets_norm_preproc_path)
min_max_scaler = joblib.load(min_max_scaler_path)
noise_cov = joblib.load(noise_cov_path)
# create iProMPs sets
ipromps_set = [
ipromps_lib.IProMP(num_joints=num_joints,
num_obs_joints=num_obs_joints,
num_basis=num_basis,
sigma_basis=sigma_basis,
num_samples=len_norm,
sigmay=noise_cov,
min_max_scaler=min_max_scaler,
num_alpha_candidate=num_alpha_candidate)
for x in datasets_norm_preproc
]
# add demo for each IProMPs
for idx, ipromp in enumerate(ipromps_set):
print('Training the IProMP for task: %s...' % task_name[idx])
# for demo_idx in datasets4train[idx]:
for demo_idx in datasets_norm_preproc[idx]:
# demo_temp = np.hstack([demo_idx['emg'], demo_idx['left_hand'], demo_idx['left_joints']])
demo_temp = np.hstack(
[demo_idx['left_hand'], demo_idx['left_joints']])
ipromp.add_demonstration(demo_temp) # spatial variance demo
ipromp.add_alpha(demo_idx['alpha']) # temporal variance demo
# save the trained models
print('Saving the trained models...')
joblib.dump(ipromps_set, os.path.join(datasets_pkl_path,
'ipromps_set.pkl'))
print('Trained the IProMPs successfully!!!')
dataset_tape_hold_norm = joblib.load('./pkl/dataset_tape_hold_norm.pkl')
#################################
# Interaction ProMPs train
#################################
# the measurement noise cov matrix
imu_meansurement_noise_cov = np.eye(4) * imu_noise
emg_meansurement_noise_cov = np.eye(8) * emg_noise
pose_meansurement_noise_cov = np.eye(7) * pose_noise
meansurement_noise_cov_full = scipy.linalg.block_diag(
imu_meansurement_noise_cov, emg_meansurement_noise_cov,
pose_meansurement_noise_cov)
# create a 3 tasks iProMP
ipromp_aluminum_hold = ipromps_lib.IProMP(num_joints=num_joints,
num_basis=num_basis,
sigma_basis=sigma_basis,
num_samples=num_samples,
num_obs_joints=num_obs_joints,
sigmay=meansurement_noise_cov_full)
ipromp_spanner_handover = ipromps_lib.IProMP(
num_joints=num_joints,
num_basis=num_basis,
sigma_basis=sigma_basis,
num_samples=num_samples,
num_obs_joints=num_obs_joints,
sigmay=meansurement_noise_cov_full)
ipromp_tape_hold = ipromps_lib.IProMP(num_joints=num_joints,
num_basis=num_basis,
sigma_basis=sigma_basis,
num_samples=num_samples,
num_obs_joints=num_obs_joints,
sigmay=meansurement_noise_cov_full)