def create_learner(self):
A, B, _ = self.decoder.ssm.get_ssm_matrices(
update_rate=self.decoder.binlen)
Q = np.mat(np.diag([1., 1, 1, 0, 0, 0, 0]))
R = self.cost_fn_scale * np.mat(np.eye(B.shape[1]))
fb_ctrl = feedback_controllers.LQRController(A, B, Q, R)
self.learner = clda.FeedbackControllerLearner(1, fb_ctrl)
self.learn_flag = True
def create_learner(self):
F, K = self.decoder.filt.get_sskf()
pos_gain = np.mean(K[[0, 2], :] / K[[3, 5], :])
I = np.mat(np.eye(3))
B = np.mat(np.vstack([pos_gain * I, I, np.zeros(3)]))
A = np.mat(F)
Q = np.mat(np.diag([1., 1, 1, 0, 0, 0, 0]))
R = np.mat(np.eye(3) * 1000)
fb_ctrl = feedback_controllers.LQRController(A, B, Q, R)
self.batch_size = int(self.batch_time / self.decoder.binlen)
self.learner = clda.FeedbackControllerLearner(self.batch_size,
fb_ctrl,
style='additive')
def create_learner(self):
self.batch_size = int(self.batch_time/self.decoder.binlen)
fb_ctrl = JointTorque2LController()
self.learner = clda.FeedbackControllerLearner(self.batch_size, fb_ctrl)
self.learn_flag = True
def create_learner(self):
self.batch_size = int(self.batch_time/self.decoder.binlen)
fb_ctrl = MSKController()
self.learner = clda.FeedbackControllerLearner(self.batch_size, fb_ctrl, style='mixing')
self.learn_flag = True
def create_learner(self):
self.batch_size = int(self.batch_time/self.decoder.binlen)
self.learner = clda.FeedbackControllerLearner(self.batch_size, mouse_motion_model)
self.learn_flag = True
def create_learner(self):
from tasks.point_mass_cursor import PointMassFBController
self.batch_size = int(self.batch_time/self.decoder.binlen)
fb_ctrl = PointMassFBController()
self.learner = clda.FeedbackControllerLearner(self.batch_size, fb_ctrl)
self.learn_flag = True
def create_learner(self):
self.batch_size = int(self.batch_time/self.decoder.binlen)
self.learner = clda.FeedbackControllerLearner(self.batch_size, joint_vel_fb_ctrl, reset_states=['go_to_origin', 'wait', 'init_exo', 'move_target', 'pause', 'reward' ])
self.learn_flag = True