def __init__(self, bfs, gain, trajectory, tau,
add_to_goals=None, **kwargs):
"""
bfs int: the number of basis functions per DMP
gain float: the PD gain while following a DMP trajectory
trajectory np.array: the time series of points to follow
[DOFs, time], with a column of None
wherever the pen should be lifted
tau float: the time scaling term
add_to_goals np.array: floats to add to the DMP goals
used to scale the DMPs spatially
"""
Control_OSC.__init__(self, **kwargs)
self.bfs = bfs
self.gain = gain
self.gen_dmps(trajectory)
self.tau = tau
self.target,_,_ = self.dmps.step(tau=self.tau)
if add_to_goals is not None:
for ii, dmp in enumerate(self.dmp_sets):
dmp.goal[0] += add_to_goals[ii*2]
dmp.goal[1] += add_to_goals[ii*2+1]
self.num_seq = 0
self.done = False
self.not_at_start = True
def __init__(self, dt, gain, trajectory, **kwargs):
Control_OSC.__init__(self, **kwargs)
self.dt = dt
self.gain = gain
self.gen_trajectories(trajectory)
self.target = np.array([self.y_des[0](0.0), self.y_des[1](0.0)])
self.num_seq = 0
self.time = 0.0
self.done = False
self.not_at_start = True
def __init__(self, dt, gain, trajectory, **kwargs):
Control_OSC.__init__(self, **kwargs)
self.dt = dt
self.gain = gain
self.gen_trajectories(trajectory)
self.target = np.array([self.y_des[0](0.0), self.y_des[1](0.0)])
self.num_seq = 0
self.time = 0.0
self.done = False
self.not_at_start = True
