class ProbabilisticObjectInput(object):
def __init__(self, name):
super(ProbabilisticObjectInput, self).__init__()
self.frame = FrameInput('{}{}frame'.format(
name, ControllerInputArray.separator))
self.dimensions = Vec3Input('{}{}dimensions'.format(
name, ControllerInputArray.separator))
self.probability_class = ScalarInput('P_class')
self.probability_pos = ScalarInput('P_trans')
self.probability_rot = ScalarInput('P_rot')
def get_update_dict(self, p_object):
out_dict = self.frame.get_update_dict(*p_object.frame)
out_dict.update(self.dimensions.get_update_dict(p_object.dimensions))
out_dict.update(self.probability_class.get_update_dict(
p_object.pclass))
out_dict.update(self.probability_pos.get_update_dict(p_object.ppos))
out_dict.update(self.probability_rot.get_update_dict(p_object.prot))
return out_dict
def get_frame(self):
return self.frame.get_expression()
def get_dimensions(self):
return self.dimensions.get_expression()
def get_class_probability(self):
return self.probability_class.get_expression()
class EEFDiffController(QPController):
def __init__(self, robot, builder_backend=None, weight=1):
self.weight = weight
super(EEFDiffController, self).__init__(robot, builder_backend)
# @profile
def add_inputs(self, robot):
self.goal_diff = FrameInput(prefix='', suffix='goal')
self.goal_weights = ScalarInput(prefix='', suffix='sc_w')
# @profile
def make_constraints(self, robot):
t = time()
eef1 = robot.end_effectors[0]
eef1_frame = robot.frames[eef1]
eef2 = robot.end_effectors[1]
eef2_frame = robot.frames[eef2]
eef_diff_pos = spw.pos_of(eef1_frame) - spw.pos_of(eef2_frame)
goal_pos = self.goal_diff.get_position()
dist = spw.norm((eef_diff_pos) - goal_pos)
eef_diff_rot = spw.rot_of(eef1_frame)[:3, :3].T * spw.rot_of(
eef2_frame)[:3, :3]
goal_rot = self.goal_diff.get_rotation()
goal_r = goal_rot[:3, :3].reshape(9, 1)
dist_r = spw.norm(eef_diff_rot.reshape(9, 1) - goal_r)
self._soft_constraints['align eefs position'] = SoftConstraint(
lower=-dist,
upper=-dist,
weight=self.goal_weights.get_expression(),
expression=dist)
self._soft_constraints['align eefs rotation'] = SoftConstraint(
lower=-dist_r,
upper=-dist_r,
weight=self.goal_weights.get_expression(),
expression=dist_r)
self._controllable_constraints = robot.joint_constraints
self._hard_constraints = robot.hard_constraints
self.update_observables(
{self.goal_weights.get_symbol_str(): self.weight})
print('make constraints took {}'.format(time() - t))
def set_goal(self, goal):
"""
dict eef_name -> goal_position
:param goal_pos:
:return:
"""
self.update_observables(self.goal_diff.get_update_dict(*goal))
def __init__(self, name):
super(ProbabilisticObjectInput, self).__init__()
self.frame = FrameInput('{}{}frame'.format(name, ControllerInputArray.separator))
self.dimensions = Vec3Input('{}{}dimensions'.format(name, ControllerInputArray.separator))
self.probability_class = ScalarInput('P_class')
self.probability_pos = ScalarInput('P_trans')
self.probability_rot = ScalarInput('P_rot')
def add_inputs(self, robot):
self.goal_eef = {}
self.goal_weights = {}
for eef in robot.end_effectors:
self.goal_eef[eef] = FrameInput(prefix=eef, suffix='goal')
self.goal_weights[eef] = ScalarInput(prefix=eef, suffix='sc_w')
def add_inputs(self, robot):
self.goal_diff = FrameInput(prefix='', suffix='goal')
self.goal_weights = ScalarInput(prefix='', suffix='sc_w')