def __init__(self, km,
actuated_pose,
goal_pose,
controlled_symbols,
weight_override=None,
draw_fn=None):
eef = actuated_pose
goal_pos_error = gm.norm(gm.pos_of(eef) - gm.pos_of(goal_pose))
self.eef_rot = eef[:3, :3]
self.goal_rot = goal_pose[:3, :3]
self.rot_error_matrix = self.eef_rot - self.goal_rot
goal_rot_error = gm.norm(self.rot_error_matrix)
rot_align_scale = gm.exp(-3 * goal_rot_error)
goal_constraints = {'align position': SC(-goal_pos_error * rot_align_scale,
-goal_pos_error * rot_align_scale, 10, goal_pos_error),
'align rotation': SC(-goal_rot_error, -goal_rot_error, 1, goal_rot_error)}
self.goal_rot_error = goal_rot_error
constraints = km.get_constraints_by_symbols(gm.free_symbols(eef).union(controlled_symbols))
cvs, constraints = generate_controlled_values(constraints, controlled_symbols, weights=weight_override)
cvs = depth_weight_controlled_values(km, cvs, exp_factor=1.02)
self.draw_fn = draw_fn
self.qp = TQPB(constraints,
goal_constraints,
cvs)
def __init__(self, km, controlled_symbols, resting_pose, camera_path=None):
tucking_constraints = {}
if resting_pose is not None:
tucking_constraints = {
f'tuck {s}': SC(p - s, p - s, 1, s)
for s, p in resting_pose.items()
}
# print('Tuck state:\n {}\nTucking constraints:\n {}'.format('\n '.join(['{}: {}'.format(k, v) for k, v in self._resting_pose.items()]), '\n '.join(tucking_constraints.keys())))
# tucking_constraints.update(self.taxi_constraints)
self.use_camera = camera_path is not None
if camera_path is not None:
self._poi_pos = gm.Symbol('poi')
poi = gm.point3(1.5, 0.5, 0.0) + gm.vector3(
0, self._poi_pos * 2.0, 0)
camera = km.get_data(camera_path)
cam_to_poi = poi - gm.pos_of(camera.pose)
lookat_dot = 1 - gm.dot_product(gm.x_of(camera.pose),
cam_to_poi) / gm.norm(cam_to_poi)
tucking_constraints['sweeping gaze'] = SC(-lookat_dot * 5,
-lookat_dot * 5, 1,
lookat_dot)
symbols = set()
for c in tucking_constraints.values():
symbols |= gm.free_symbols(c.expr)
joint_symbols = {
s
for s in symbols if gm.get_symbol_type(s) != gm.TYPE_UNKNOWN
}
controlled_symbols = {gm.DiffSymbol(s) for s in joint_symbols}
hard_constraints = km.get_constraints_by_symbols(
symbols.union(controlled_symbols))
controlled_values, hard_constraints = generate_controlled_values(
hard_constraints, controlled_symbols)
controlled_values = depth_weight_controlled_values(
km, controlled_values)
self.qp = TQPB(hard_constraints, tucking_constraints,
controlled_values)
self._start = Time.now()
def test_revolute_and_continuous_joint(self):
ks = ArticulationModel()
a = Position('a')
b = Position('b')
c = Position('c')
parent_pose = frame3_axis_angle(vector3(0,1,0), a, point3(0, b, 5))
joint_transform = translation3(7, -5, 33)
axis = vector3(1, -3, 7)
axis = axis / norm(axis)
position = c
child_pose = parent_pose * joint_transform * rotation3_axis_angle(axis, position)
ks.apply_operation('create parent', CreateComplexObject(Path('parent'), KinematicLink('', parent_pose)))
ks.apply_operation('create child', CreateComplexObject(Path('child'), KinematicLink('', se.eye(4))))
self.assertTrue(ks.has_data('parent/pose'))
self.assertTrue(ks.has_data('child/pose'))
ks.apply_operation('connect parent child',
SetRevoluteJoint(Path('parent/pose'),
Path('child/pose'),
Path('fixed_joint'),
joint_transform,
axis,
position,
-1, 2, 0.5))
self.assertTrue(ks.has_data('fixed_joint'))
self.assertEquals(ks.get_data('child/pose'), child_pose)
ks.remove_operation('connect parent child')
ks.apply_operation('connect parent child',
SetContinuousJoint(Path('parent/pose'),
Path('child/pose'),
Path('fixed_joint'),
joint_transform,
axis,
position, 0.5))
collision_world.update_world(start_state)
vis.begin_draw_cycle('ik_solution')
vis.draw_world('ik_solution', collision_world)
vis.render('ik_solution')
print(f'IK error: {ik_err}')
def gen_dv_cvs(km, constraints, controlled_symbols):
cvs, constraints = generate_controlled_values(constraints, controlled_symbols)
cvs = depth_weight_controlled_values(km, cvs, exp_factor=1.02)
print('\n'.join(f'{cv.symbol}: {cv.weight_id}' for _, cv in sorted(cvs.items())))
return cvs, constraints
dyn_goal_pos_error = gm.norm(gm.pos_of(eef.pose) - gm.pos_of(goal_pose))
dyn_goal_rot_error = gm.norm(eef.pose[:3, :3] - goal_pose[:3, :3])
o_vars, bounds, _ = static_var_bounds(km, gm.free_symbols(handle.pose))
lead_goal_constraints = {f'open_object {s}': SC(ub - s,
ub - s, 1, s) for s, (_, ub) in zip(o_vars, bounds)}
follower_goal_constraints = {'keep position': SC(-dyn_goal_pos_error,
-dyn_goal_pos_error, 10, dyn_goal_pos_error),
'keep rotation': SC(-dyn_goal_rot_error, -dyn_goal_rot_error, 1, dyn_goal_rot_error)}
solver = CascadingQP(km,
lead_goal_constraints,
follower_goal_constraints,
f_gen_follower_cvs=gen_dv_cvs,
def behavior_update(self):
state_count = 0
while not rospy.is_shutdown() and not self._kys:
loop_start = rospy.Time.now()
with self._state_lock:
if self._robot_state_update_count <= state_count:
rospy.sleep(0.01)
continue
state_count = self._robot_state_update_count
if self.controller is not None:
now = rospy.Time.now()
with self._state_lock:
deltaT = 0.05 if self._last_controller_update is None else (
now - self._last_controller_update).to_sec()
try:
command = self.controller.get_cmd(self._state,
deltaT=deltaT)
except Exception as e:
print(traceback.format_exc())
rospy.signal_shutdown('die lol')
self.robot_command_processor.send_command(command)
self._last_controller_update = now
# Lets not confuse the tracker
if self._phase != 'opening' and self._last_external_cmd_msg is not None:
# Ensure that velocities are assumed to be 0 when not operating anything
self._last_external_cmd_msg.value = [0] * len(
self._last_external_cmd_msg.value)
self.pub_external_command.publish(self._last_external_cmd_msg)
self._last_external_cmd_msg = None
if self._phase == 'idle':
# if there is no controller, instantiate idle
# check for new target
# -> open gripper
# instantiate 6d controller
# switch to "grasping"
if self.controller is None:
self.gripper_wrapper.sync_set_gripper_position(0.07)
self.controller = self._idle_controller
with self._state_lock:
for s, p in self._target_map.items():
if s in self._state and self._state[
s] < self._var_upper_bound[
s] * self.monitoring_threshold: # Some thing in the scene is closed
self._current_target = p
print(f'New target is {self._current_target}')
self.gripper_wrapper.sync_set_gripper_position(
0.07)
self._last_controller_update = None
print('Gripper is open. Proceeding to grasp...')
draw_fn = None
eef_pose = self.km.get_data(self.eef_path).pose
if self.visualizer is not None:
self.visualizer.begin_draw_cycle('grasp pose')
self.visualizer.draw_poses(
'grasp pose', np.eye(4), 0.2, 0.01, [
gm.subs(self._grasp_poses[p],
self._state)
])
self.visualizer.render('grasp pose')
def draw_fn(state):
self.visualizer.begin_draw_cycle(
'debug_grasp')
self.visualizer.draw_poses(
'debug_grasp', np.eye(4), 1.0, 0.01, [
gm.subs(eef_pose, state),
gm.subs(self._grasp_poses[p],
state)
])
self.visualizer.render('debug_grasp')
# print('\n'.join(f'{s}: {v}' for s, v in state.items()))
self.controller = SixDPoseController(
self.km, eef_pose, self._grasp_poses[p],
self.controlled_symbols, self.weights, draw_fn)
self._phase = 'grasping'
print(f'Now entering {self._phase} state')
break
else:
print(
'None of the monitored symbols are closed:\n {}'.
format('\n '.join(
f'{self._state[s]} < {self._var_upper_bound[s]}'
for s in self._target_map.keys()
if s in self._state)))
elif self._phase == 'grasping':
# check if grasp is acheived
# -> close gripper
# instantiate cascading controller
# switch to "opening"
# if there is no more command but the goal error is too great -> "homing"
if self.controller.equilibrium_reached(0.03, -0.03):
if self.controller.current_error() > 0.01:
self.controller = self._idle_controller
self._phase = 'homing'
print(f'Now entering {self._phase} state')
else:
print('Closing gripper...')
self.gripper_wrapper.sync_set_gripper_position(0, 80)
print('Generating opening controller')
eef = self.km.get_data(self.eef_path)
obj = self.km.get_data(self._current_target)
with self._state_lock:
static_eef_pose = gm.subs(eef.pose, self._state)
static_object_pose = gm.subs(obj.pose, self._state)
offset_pose = np_inverse_frame(static_object_pose).dot(
static_eef_pose)
print(offset_pose)
goal_pose = gm.dot(obj.pose, gm.Matrix(offset_pose))
goal_pos_error = gm.norm(
gm.pos_of(eef.pose) - gm.pos_of(goal_pose))
goal_rot_error = gm.norm(eef.pose[:3, :3] -
goal_pose[:3, :3])
target_symbols = self._target_body_map[
self._current_target]
lead_goal_constraints = {
f'open_{s}': SC(self._var_upper_bound[s] - s, 1000,
1, s)
for s in target_symbols
if s in self._var_upper_bound
}
for n, c in lead_goal_constraints.items():
print(f'{n}: {c}')
follower_goal_constraints = {
'keep position':
SC(-goal_pos_error, -goal_pos_error, 10,
goal_pos_error),
'keep rotation':
SC(-goal_rot_error, -goal_rot_error, 1,
goal_rot_error)
}
blacklist = {
gm.Velocity(Path('pr2/torso_lift_joint'))
}.union({
gm.DiffSymbol(s)
for s in gm.free_symbols(obj.pose)
if 'location' in str(s)
})
# blacklist = {gm.DiffSymbol(s) for s in gm.free_symbols(obj.pose) if 'location' in str(s)}
self.controller = CascadingQP(
self.km,
lead_goal_constraints,
follower_goal_constraints,
f_gen_follower_cvs=gen_dv_cvs,
controls_blacklist=blacklist,
t_follower=GQPB,
visualizer=None # self.visualizer
)
print(self.controller)
# def debug_draw(vis, state, cmd):
# vis.begin_draw_cycle('lol')
# vis.draw_poses('lol', np.eye(4), 0.2, 0.01,
# [gm.subs(goal_pose, state),
# gm.subs(eef.pose, state)])
# vis.render('lol')
# self.controller.follower_qp._cb_draw = debug_draw
# self.gripper_wrapper.sync_set_gripper_position(0.07)
# rospy.signal_shutdown('die lol')
# return
self._last_controller_update = None
self._phase = 'opening'
print(f'Now entering {self._phase} state')
elif self._phase == 'opening':
# Wait for monitored symbols to be in open position
# -> open gripper
# generate 6d retraction goal: -10cm in tool frame
# spawn 6d controller
# switch to "retracting"
# self.visualizer.begin_draw_cycle('world state')
# with self._state_lock:
# self._world.update_world(self._state)
# self.visualizer.draw_world('world state', self._world, b=0)
# self.visualizer.render('world state')
external_command = {
s: v
for s, v in command.items()
if s not in self.controlled_symbols
}
ext_msg = ValueMapMsg()
ext_msg.header.stamp = now
ext_msg.symbol, ext_msg.value = zip(
*[(str(s), v) for s, v in external_command.items()])
self.pub_external_command.publish(ext_msg)
self._last_external_cmd_msg = ext_msg
with self._state_lock:
current_external_error = {
s: self._state[s]
for s in self._target_body_map[self._current_target]
}
print('Current error state:\n {}'.format('\n '.join(
[f'{s}: {v}' for s, v in current_external_error.items()])))
gripper_err = self.gripper_wrapper.get_latest_error()
# if gripper_err < 0.0005: # Grasped object is thinner than 5mm aka we lost it
# self.controller = self._idle_controller
# self._phase = 'homing'
# print(f'Grasped object seems to have slipped ({gripper_err}). Returning to home...')
# return
if min([
v >=
self._var_upper_bound[s] * self.acceptance_threshold
for s, v in current_external_error.items()
]):
print('Target fulfilled. Setting it to None')
self._current_target = None
eef = self.km.get_data(self.eef_path)
with self._state_lock:
static_eef_pose = gm.subs(eef.pose, self._state)
goal_pose = static_eef_pose.dot(
np.array([[1, 0, 0, -0.12], [0, 1, 0, 0], [0, 0, 1, 0],
[0, 0, 0, 1]]))
self.controller = SixDPoseController(
self.km, eef.pose, goal_pose, self.controlled_symbols,
self.weights)
self.gripper_wrapper.sync_set_gripper_position(0.07)
self._last_controller_update = None
self._phase = 'retracting'
print(f'Now entering {self._phase} state')
else:
remainder = (1 / 3) - (rospy.Time.now() -
loop_start).to_sec()
if remainder > 0:
rospy.sleep(remainder)
elif self._phase == 'retracting':
# Wait for retraction to complete (Currently just skipping)
# -> spawn idle controller
# switch to "homing"
if self.controller.equilibrium_reached(0.035, -0.035):
self.controller = self._idle_controller
self._phase = 'homing'
print(f'Now entering {self._phase} state')
elif self._phase == 'homing':
# Wait for idle controller to have somewhat low error
# -> switch to "idle"
if self.controller is None:
self.gripper_wrapper.sync_set_gripper_position(0.07)
self.controller = self._idle_controller
continue
if self.controller.equilibrium_reached(0.1, -0.1):
self._phase = 'idle'
print(f'Now entering {self._phase} state')
else:
raise Exception(f'Unknown state "{self._phase}')
def create_nobilia_shelf(km,
prefix,
origin_pose=gm.eye(4),
parent_path=Path('world')):
km.apply_operation(
f'create {prefix}',
ExecFunction(prefix, MarkedArticulatedObject, str(prefix)))
shelf_height = 0.72
shelf_width = 0.6
shelf_body_depth = 0.35
wall_width = 0.016
l_prefix = prefix + ('links', )
geom_body_wall_l = Box(
l_prefix + ('body', ),
gm.translation3(0, 0.5 * (shelf_width - wall_width), 0),
gm.vector3(shelf_body_depth, wall_width, shelf_height))
geom_body_wall_r = Box(
l_prefix + ('body', ),
gm.translation3(0, -0.5 * (shelf_width - wall_width), 0),
gm.vector3(shelf_body_depth, wall_width, shelf_height))
geom_body_ceiling = Box(
l_prefix + ('body', ),
gm.translation3(0, 0, 0.5 * (shelf_height - wall_width)),
gm.vector3(shelf_body_depth, shelf_width - wall_width, wall_width))
geom_body_floor = Box(
l_prefix + ('body', ),
gm.translation3(0, 0, -0.5 * (shelf_height - wall_width)),
gm.vector3(shelf_body_depth, shelf_width - wall_width, wall_width))
geom_body_shelf_1 = Box(
l_prefix + ('body', ),
gm.translation3(0.02, 0, -0.2 * (shelf_height - wall_width)),
gm.vector3(shelf_body_depth - 0.04, shelf_width - wall_width,
wall_width))
geom_body_shelf_2 = Box(
l_prefix + ('body', ),
gm.translation3(0.02, 0, 0.2 * (shelf_height - wall_width)),
gm.vector3(shelf_body_depth - 0.04, shelf_width - wall_width,
wall_width))
geom_body_back = Box(
l_prefix + ('body', ),
gm.translation3(0.5 * (shelf_body_depth - 0.005), 0, 0),
gm.vector3(0.005, shelf_width - 2 * wall_width,
shelf_height - 2 * wall_width))
shelf_geom = [
geom_body_wall_l, geom_body_wall_r, geom_body_ceiling, geom_body_floor,
geom_body_back, geom_body_shelf_1, geom_body_shelf_2
]
rb_body = RigidBody(parent_path,
origin_pose,
geometry=dict(enumerate(shelf_geom)),
collision=dict(enumerate(shelf_geom)))
geom_panel_top = Box(l_prefix + ('panel_top', ), gm.eye(4),
gm.vector3(0.357, 0.595, wall_width))
geom_panel_bottom = Box(l_prefix + ('panel_bottom', ), gm.eye(4),
gm.vector3(0.357, 0.595, wall_width))
handle_width = 0.16
handle_depth = 0.05
handle_diameter = 0.012
geom_handle_r = Box(
l_prefix + ('handle', ),
gm.translation3(0.5 * handle_depth,
0.5 * (handle_width - handle_diameter), 0),
gm.vector3(handle_depth, handle_diameter, handle_diameter))
geom_handle_l = Box(
l_prefix + ('handle', ),
gm.translation3(0.5 * handle_depth,
-0.5 * (handle_width - handle_diameter), 0),
gm.vector3(handle_depth, handle_diameter, handle_diameter))
geom_handle_bar = Box(
l_prefix + ('handle', ),
gm.translation3(handle_depth - 0.5 * handle_diameter, 0, 0),
gm.vector3(handle_diameter, handle_width - handle_diameter,
handle_diameter))
handle_geom = [geom_handle_l, geom_handle_r, geom_handle_bar]
# Sketch of mechanism
#
# T ---- a
# ---- \ Z
# b ..... V \
# | ...... d
# B | ------
# c ------
# L
#
# Diagonal V is virtual
#
#
# Angles:
# a -> alpha (given)
# b -> gamma_1 + gamma_2 = gamma
# c -> don't care
# d -> delta_1 + delta_2 = delta
#
opening_position = gm.Position(prefix + ('door', ))
# Calibration results
#
# Solution top hinge: cost = 0.03709980624159568 [ 0.08762252 -0.01433833 0.2858676 0.00871125]
# Solution bottom hinge: cost = 0.025004236048128934 [ 0.1072496 -0.01232362 0.27271013 0.00489996]
# Added 180 deg rotation due to -x being the forward facing side in this model
top_hinge_in_body_marker = gm.translation3(0.08762252 - 0.015, 0,
-0.01433833)
top_panel_marker_in_top_hinge = gm.translation3(0.2858676 - 0.003,
-wall_width + 0.0025,
0.00871125 - 0.003)
front_hinge_in_top_panel_maker = gm.translation3(0.1072496 - 0.02, 0,
-0.01232362 + 0.007)
bottom_panel_marker_in_front_hinge = gm.translation3(
0.27271013, 0, 0.00489996)
# Top hinge - Data taken from observation
body_marker_in_body = gm.dot(
gm.rotation3_axis_angle(gm.vector3(0, 0, 1), math.pi),
gm.translation3(0.5 * shelf_body_depth - 0.062,
-0.5 * shelf_width + 0.078, 0.5 * shelf_height))
top_panel_marker_in_top_panel = gm.translation3(
geom_panel_top.scale[0] * 0.5 - 0.062,
-geom_panel_top.scale[1] * 0.5 + 0.062, geom_panel_top.scale[2] * 0.5)
bottom_panel_marker_in_bottom_panel = gm.translation3(
geom_panel_bottom.scale[0] * 0.5 - 0.062,
-geom_panel_bottom.scale[1] * 0.5 + 0.062,
geom_panel_bottom.scale[2] * 0.5)
top_hinge_in_body = gm.dot(body_marker_in_body, top_hinge_in_body_marker)
top_panel_in_top_hinge = gm.dot(
top_panel_marker_in_top_hinge,
gm.inverse_frame(top_panel_marker_in_top_panel))
front_hinge_in_top_panel = gm.dot(top_panel_marker_in_top_panel,
front_hinge_in_top_panel_maker)
bottom_panel_in_front_hinge = gm.dot(
bottom_panel_marker_in_front_hinge,
gm.inverse_frame(bottom_panel_marker_in_bottom_panel))
# Point a in body reference frame
point_a = gm.dot(gm.diag(1, 0, 1, 1), gm.pos_of(top_hinge_in_body))
point_d = gm.point3(-shelf_body_depth * 0.5 + 0.09, 0,
shelf_height * 0.5 - 0.192)
# point_d = gm.point3(-shelf_body_depth * 0.5 + gm.Symbol('point_d_x'), 0, shelf_height * 0.5 - gm.Symbol('point_d_z'))
# Zero alpha along the vertical axis
vec_a_to_d = gm.dot(point_d - point_a)
alpha = gm.atan2(vec_a_to_d[0], -vec_a_to_d[2]) + opening_position
top_panel_in_body = gm.dot(
top_hinge_in_body, # Translation hinge to body frame
gm.rotation3_axis_angle(gm.vector3(0, 1, 0), -opening_position +
0.5 * math.pi), # Hinge around y
top_panel_in_top_hinge)
front_hinge_in_body = gm.dot(top_panel_in_body, front_hinge_in_top_panel)
# Point b in top panel reference frame
point_b_in_top_hinge = gm.pos_of(
gm.dot(gm.diag(1, 0, 1, 1), front_hinge_in_top_panel,
top_panel_in_top_hinge))
point_b = gm.dot(gm.diag(1, 0, 1, 1), gm.pos_of(front_hinge_in_body))
# Hinge lift arm in body reference frame
point_c_in_bottom_panel = gm.dot(
gm.diag(1, 0, 1, 1),
bottom_panel_marker_in_bottom_panel,
gm.point3(-0.094, -0.034, -0.072),
# gm.point3(-gm.Symbol('point_c_x'), -0.034, -gm.Symbol('point_c_z'))
)
point_c_in_front_hinge = gm.dot(
gm.diag(1, 0, 1, 1),
gm.dot(bottom_panel_in_front_hinge, point_c_in_bottom_panel))
length_z = gm.norm(point_a - point_d)
vec_a_to_b = point_b - point_a
length_t = gm.norm(vec_a_to_b)
length_b = gm.norm(point_c_in_front_hinge[:3])
# length_l = gm.Symbol('length_l') # 0.34
length_l = 0.372
vec_b_to_d = point_d - point_b
length_v = gm.norm(vec_b_to_d)
gamma_1 = inner_triangle_angle(length_t, length_v, length_z)
gamma_2 = inner_triangle_angle(length_b, length_v, length_l)
top_panel_offset_angle = gm.atan2(point_b_in_top_hinge[2],
point_b_in_top_hinge[0])
bottom_offset_angle = gm.atan2(point_c_in_front_hinge[2],
point_c_in_front_hinge[0])
gamma = gamma_1 + gamma_2
rb_panel_top = RigidBody(l_prefix + ('body', ),
gm.dot(rb_body.pose, top_panel_in_body),
top_panel_in_body,
geometry={0: geom_panel_top},
collision={0: geom_panel_top})
# old offset: 0.5 * geom_panel_top.scale[2] + 0.03
tf_bottom_panel = gm.dot(
front_hinge_in_top_panel,
gm.rotation3_axis_angle(
gm.vector3(0, 1, 0),
math.pi + bottom_offset_angle - top_panel_offset_angle),
gm.rotation3_axis_angle(gm.vector3(0, -1, 0), gamma),
bottom_panel_in_front_hinge)
rb_panel_bottom = RigidBody(l_prefix + ('panel_top', ),
gm.dot(rb_panel_top.pose, tf_bottom_panel),
tf_bottom_panel,
geometry={0: geom_panel_bottom},
collision={0: geom_panel_bottom})
handle_transform = gm.dot(
gm.translation3(geom_panel_bottom.scale[0] * 0.5 - 0.08, 0,
0.5 * wall_width),
gm.rotation3_axis_angle(gm.vector3(0, 1, 0), -math.pi * 0.5))
rb_handle = RigidBody(l_prefix + ('panel_bottom', ),
gm.dot(rb_panel_bottom.pose, handle_transform),
handle_transform,
geometry={x: g
for x, g in enumerate(handle_geom)},
collision={x: g
for x, g in enumerate(handle_geom)})
# Only debugging
point_c = gm.dot(rb_panel_bottom.pose, point_c_in_bottom_panel)
vec_b_to_c = point_c - point_b
km.apply_operation(f'create {prefix}/links/body',
CreateValue(rb_panel_top.parent, rb_body))
km.apply_operation(f'create {prefix}/links/panel_top',
CreateValue(rb_panel_bottom.parent, rb_panel_top))
km.apply_operation(
f'create {prefix}/links/panel_bottom',
CreateValue(l_prefix + ('panel_bottom', ), rb_panel_bottom))
km.apply_operation(f'create {prefix}/links/handle',
CreateValue(l_prefix + ('handle', ), rb_handle))
km.apply_operation(
f'create {prefix}/joints/hinge',
ExecFunction(
prefix + Path('joints/hinge'), RevoluteJoint,
CPath(rb_panel_top.parent), CPath(rb_panel_bottom.parent),
opening_position, gm.vector3(0, 1, 0), gm.eye(4), 0, 1.84, **{
f'{opening_position}':
Constraint(0 - opening_position, 1.84 - opening_position,
opening_position),
f'{gm.DiffSymbol(opening_position)}':
Constraint(-0.25, 0.25, gm.DiffSymbol(opening_position))
}))
m_prefix = prefix + ('markers', )
km.apply_operation(
f'create {prefix}/markers/body',
ExecFunction(m_prefix + ('body', ), Frame,
CPath(l_prefix + ('body', )),
gm.dot(rb_body.pose,
body_marker_in_body), body_marker_in_body))
km.apply_operation(
f'create {prefix}/markers/top_panel',
ExecFunction(m_prefix + ('top_panel', ), Frame,
CPath(l_prefix + ('panel_top', )),
gm.dot(rb_panel_top.pose, top_panel_marker_in_top_panel),
top_panel_marker_in_top_panel))
km.apply_operation(
f'create {prefix}/markers/bottom_panel',
ExecFunction(
m_prefix + ('bottom_panel', ), Frame,
CPath(l_prefix + ('panel_bottom', )),
gm.dot(rb_panel_bottom.pose, bottom_panel_marker_in_bottom_panel),
bottom_panel_marker_in_bottom_panel))
return NobiliaDebug(
[
top_hinge_in_body,
gm.dot(
top_hinge_in_body,
gm.rotation3_axis_angle(gm.vector3(0, 1, 0),
-opening_position + 0.5 * math.pi),
top_panel_in_top_hinge, front_hinge_in_top_panel),
body_marker_in_body,
gm.dot(rb_panel_top.pose, top_panel_marker_in_top_panel),
gm.dot(rb_panel_bottom.pose, bottom_panel_marker_in_bottom_panel)
], [(point_a, vec_a_to_d), (point_a, vec_a_to_b),
(point_b, vec_b_to_d), (point_b, vec_b_to_c)],
{
'gamma_1':
gamma_1,
'gamma_1 check_dot':
gamma_1 - gm.acos(
gm.dot_product(-vec_a_to_b / gm.norm(vec_a_to_b),
vec_b_to_d / gm.norm(vec_b_to_d))),
'gamma_1 check_cos':
gamma_1 - inner_triangle_angle(
gm.norm(vec_a_to_b), gm.norm(vec_b_to_d), gm.norm(vec_a_to_d)),
'gamma_2':
gamma_2,
'gamma_2 check_dot':
gamma_2 - gm.acos(
gm.dot_product(vec_b_to_c / gm.norm(vec_b_to_c),
vec_b_to_d / gm.norm(vec_b_to_d))),
'length_v':
length_v,
'length_b':
length_b,
'length_l':
length_l,
'position':
opening_position,
'alpha':
alpha,
'dist c d':
gm.norm(point_d - point_c)
}, {
gm.Symbol('point_c_x'): 0.094,
gm.Symbol('point_c_z'): 0.072,
gm.Symbol('point_d_x'): 0.09,
gm.Symbol('point_d_z'): 0.192,
gm.Symbol('length_l'): 0.372
})
def generate_push_closing(km,
grounding_state,
controlled_symbols,
eef_pose,
obj_pose,
eef_path,
obj_path,
nav_method='cross',
cp_offset=0,
static_symbols=set()):
# CONTACT GEOMETRY
robot_cp, object_cp, contact_normal = contact_geometry(
eef_pose, obj_pose, eef_path, obj_path)
object_cp = object_cp - contact_normal * cp_offset
geom_distance = gm.dot_product(contact_normal, robot_cp - object_cp)
coll_world = km.get_active_geometry(gm.free_symbols(geom_distance))
# GEOMETRY NAVIGATION LOGIC
# This is exploiting task knowledge which makes this function inflexible.
contact_grad = sum([
sign(-grounding_state[s]) *
gm.vector3(gm.diff(object_cp[0], s), gm.diff(object_cp[1], s),
gm.diff(object_cp[2], s))
for s in gm.free_symbols(obj_pose) if s not in static_symbols
], gm.vector3(0, 0, 0))
neutral_tangent = gm.cross(contact_grad, contact_normal)
active_tangent = gm.cross(neutral_tangent, contact_normal)
contact_constraints, in_contact = generate_contact_model(
robot_cp, controlled_symbols, object_cp, contact_normal,
gm.free_symbols(obj_pose))
target_pos = None
if nav_method == 'linear':
geom_distance = gm.norm(object_cp + active_tangent * geom_distance +
contact_grad * 0.05 - robot_cp)
elif nav_method == 'cubic':
dist_scaling = 2**(-0.5 * ((geom_distance - 0.2) / (0.2 * 0.2))**2)
geom_distance = gm.norm(object_cp + active_tangent * dist_scaling -
robot_cp)
elif nav_method == 'cross':
geom_distance = gm.norm(object_cp +
active_tangent * gm.norm(neutral_tangent) +
contact_grad * 0.05 - robot_cp)
elif nav_method == 'cross_deep':
geom_distance = gm.norm(object_cp +
active_tangent * gm.norm(neutral_tangent) +
contact_grad *
-gm.dot_product(contact_normal, contact_grad) -
robot_cp)
elif nav_method == 'none' or nav_method is None:
pass
elif nav_method == 'proj':
obj_cp_dist = gm.dot_product(contact_normal,
object_cp - gm.pos_of(obj_pose))
target_pos = gm.pos_of(
obj_pose
) + contact_normal * obj_cp_dist - contact_normal * 0.02 # Drive into the surface
geom_distance = gm.norm(robot_cp - target_pos)
contact_relative_pos = gm.dot(gm.rot_of(obj_pose),
robot_cp - gm.pos_of(obj_pose))
contact_relative_vel = gm.vector3(
sum([gm.diff(contact_relative_pos[0], s) for s in controlled_symbols],
0),
sum([gm.diff(contact_relative_pos[1], s) for s in controlled_symbols],
0),
sum([gm.diff(contact_relative_pos[2], s) for s in controlled_symbols],
0))
# PUSH CONSTRAINT GENERATION
constraints = km.get_constraints_by_symbols(
gm.free_symbols(geom_distance).union(controlled_symbols))
constraints.update(contact_constraints)
# for x, n in enumerate('xyz'):
# constraints[f'zero tangent vel_{n}'] = Constraint((1 - in_contact) * -1e3,
# (1 - in_contact) * 1e3, contact_relative_pos[x] * (1.0 + 0.1 * x))
def debug_draw(vis, state, cmd):
vis.begin_draw_cycle('debug_vecs')
s_object_cp = gm.subs(object_cp, state)
s_neutral_tangent = gm.subs(neutral_tangent, state)
vis.draw_vector('debug_vecs',
s_object_cp,
gm.subs(contact_grad, state),
r=0,
b=0)
vis.draw_vector('debug_vecs',
s_object_cp,
gm.subs(active_tangent, state),
r=0,
b=1)
vis.draw_vector('debug_vecs',
s_object_cp,
gm.subs(neutral_tangent, state),
r=1,
g=1,
b=0)
if target_pos is not None:
vis.draw_sphere('debug_vecs',
gm.subs(target_pos, state),
0.01,
r=0,
b=1)
# print(f'{gm.norm(gm.subs(contact_normal, state))}')
# vis.draw_vector('debug_vecs', s_object_cp, s_ortho_vel_vec, r=1, b=0)
vis.render('debug_vecs')
return constraints, geom_distance, coll_world, PushingInternals(
robot_cp, contact_normal, contact_relative_pos, contact_relative_vel,
debug_draw)
def __init__(self,
km,
actuated_object_path,
target_object_path,
controlled_symbols,
start_state,
camera_path=None,
navigation_method='cross',
visualizer=None,
weight_override=None,
collision_avoidance_paths=None):
print(f'{actuated_object_path}\n{target_object_path}')
actuated_object = km.get_data(actuated_object_path)
target_object = km.get_data(target_object_path)
all_controlled_symbols = controlled_symbols.union({
gm.DiffSymbol(j)
for j in gm.free_symbols(target_object.pose)
if 'location' not in str(j)
})
static_symbols = {
s
for s in gm.free_symbols(target_object.pose)
if 'location' in str(s)
}
# Generate push problem
constraints, \
geom_distance, \
coll_world, \
self.p_internals = generate_push_closing(km,
start_state,
all_controlled_symbols,
actuated_object.pose,
target_object.pose,
actuated_object_path,
target_object_path,
navigation_method,
static_symbols=static_symbols)
start_state.update({s: 0.0 for s in gm.free_symbols(coll_world)})
weight_override = {} if weight_override is None else weight_override
controlled_values, \
constraints = generate_controlled_values(constraints,
all_controlled_symbols)
controlled_values = depth_weight_controlled_values(km,
controlled_values,
exp_factor=1.1)
goal_constraints = {
'reach_point': PIDC(geom_distance, geom_distance, 1, k_i=0.00)
}
for s, w in weight_override.items():
for cv in controlled_values.values():
if cv.symbol is s:
cv.weight_id = w
break
# CAMERA STUFF
if camera_path is not None:
camera = km.get_data(camera_path)
cam_pos = gm.pos_of(camera.pose)
cam_to_obj = gm.pos_of(target_object.pose) - cam_pos
cam_forward = gm.dot(camera.pose, gm.vector3(1, 0, 0))
look_goal = 1 - (gm.dot_product(cam_to_obj, cam_forward) /
gm.norm(cam_to_obj))
goal_constraints['look_at_obj'] = SC(-look_goal, -look_goal, 1,
look_goal)
# GOAL CONSTAINT GENERATION
# 'avoid_collisions': SC.from_constraint(closest_distance_constraint_world(eef_pose, eef_path[:-1], 0.03), 100)
# }
if collision_avoidance_paths is not None:
for p in collision_avoidance_paths:
obj = km.get_data(p)
goal_constraints[f'avoid_collision {p}'] = SC.from_constraint(
closest_distance_constraint(actuated_object.pose, obj.pose,
actuated_object_path, p, 0.01),
100)
goal_constraints.update({
f'open_object_{x}': PIDC(s, s, 1)
for x, s in enumerate(gm.free_symbols(target_object.pose))
})
self.look_goal = look_goal if camera_path is not None else None
self.in_contact = gm.less_than(geom_distance, 0.01)
self.controlled_values = controlled_values
self.geom_distance = geom_distance
self.qpb = GQPB(coll_world,
constraints,
goal_constraints,
controlled_values,
visualizer=visualizer)
self.qpb._cb_draw = self.p_internals.f_debug_draw
def post_physics_update(self, simulator, deltaT):
"""Implements post physics step behavior.
:type simulator: BasicSimulator
:type deltaT: float
"""
if not self._enabled:
return
self._last_update += deltaT
if self._last_update >= self._update_wait:
self._last_update = 0
cf_tuple = self.multibody.get_link_state(self.camera_link).worldFrame
camera_frame = gm.frame3_quaternion(cf_tuple.position.x, cf_tuple.position.y, cf_tuple.position.z, *cf_tuple.quaternion)
cov_proj = gm.rot_of(camera_frame)[:3, :3]
inv_cov_proj = cov_proj.T
out = PSAMsg()
if self.visualizer is not None:
self.visualizer.begin_draw_cycle()
poses = []
for name, body in simulator.bodies.items():
if body == self.multibody:
continue
if isinstance(body, MultiBody):
poses_to_process = [('{}/{}'.format(name, l), body.get_link_state(l).worldFrame) for l in body.links]
else:
poses_to_process = [(name, body.pose())]
for pname, pose in poses_to_process:
if not pname in self.message_templates:
msg = PoseStampedMsg()
msg.header.frame_id = pname
self.message_templates[pname] = msg
else:
msg = self.message_templates[pname]
obj_pos = gm.point3(*pose.position)
c2o = obj_pos - gm.pos_of(camera_frame)
dist = gm.norm(c2o)
if dist < self.far and dist > self.near and gm.dot_product(c2o, gm.x_of(camera_frame)) > gm.cos(self.fov * 0.5) * dist:
noise = 2 ** (self.noise_exp * dist) - 1
(n_quat, ) = np_random_quat_normal(1, 0, noise)
(n_trans, ) = np_random_normal_offset(1, 0, noise)
n_pose = pb.Transform(pb.Quaternion(*pose.quaternion), pb.Vector3(*pose.position)) *\
pb.Transform(pb.Quaternion(*n_quat), pb.Vector3(*n_trans[:3]))
if self.visualizer is not None:
poses.append(transform_to_matrix(n_pose))
msg.pose.position.x = n_pose.origin.x
msg.pose.position.y = n_pose.origin.y
msg.pose.position.z = n_pose.origin.z
msg.pose.orientation.x = n_pose.rotation.x
msg.pose.orientation.y = n_pose.rotation.y
msg.pose.orientation.z = n_pose.rotation.z
msg.pose.orientation.w = n_pose.rotation.w
out.poses.append(msg)
self.publisher.publish(out)
if self.visualizer is not None:
self.visualizer.draw_poses('debug', gm.se.eye(4), 0.1, 0.02, poses)
self.visualizer.render()
collision_avoidance_paths=active_parts_to_avoid)
if robot in start_poses:
start_state.update({
gm.Position(Path(robot) + (k, )): v
for k, v in start_poses[robot].items()
})
start_state.update({
c.symbol: 0.0
for c in pushing_controller.controlled_values.values()
})
# start_state.update({s: 1.84 for s in gm.free_symbols(obj.pose)})
start_state.update({s: 0.4 for s in gm.free_symbols(obj.pose)})
printed_exprs['relative_vel'] = gm.norm(
pushing_controller.p_internals.relative_pos)
integrator = CommandIntegrator(pushing_controller.qpb,
integration_rules,
equilibrium=0.0004,
start_state=start_state,
recorded_terms={
'distance':
pushing_controller.geom_distance,
'gaze_align':
pushing_controller.look_goal,
'in contact':
pushing_controller.in_contact,
'location_x': base_joint.x_pos,
'location_y': base_joint.y_pos,
'rotation_a': base_joint.a_pos
def _generate_pose_constraints(self, str_path, model):
with self.lock:
if str_path in self.tracked_poses:
align_rotation = '{} align rotation'.format(str_path)
align_position = '{} align position'.format(str_path)
if model is not None:
m_free_symbols = cm.free_symbols(model.pose)
if len(m_free_symbols) > 0:
te = self.tracked_poses[str_path]
self.joints |= m_free_symbols
self.joint_aliases = {s: str(s) for s in self.joints}
r_dist = norm(
cm.rot_of(model.pose) - cm.rot_of(te.pose))
self.soft_constraints[align_rotation] = SC(
-r_dist, -r_dist, 1, r_dist)
# print(align_position)
# print(model.pose)
dist = norm(cm.pos_of(model.pose) - cm.pos_of(te.pose))
# print('Distance expression:\n{}'.format(dist))
# print('Distance expression symbol overlap:\n{}'.format(m_free_symbols.intersection(cm.free_symbols(dist))))
# for s in m_free_symbols:
# print('Diff w.r.t {}:\n{}'.format(s, cm.diff(dist, s)))
self.soft_constraints[align_position] = SC(
-dist, -dist, 1, dist)
self.generate_opt_problem()
# Avoid crashes due to insufficient perception data. This is not fully correct.
if str_path in self._unintialized_poses:
state = self.integrator.state.copy(
) if self.integrator is not None else {}
state.update({
s: 0.0
for s in m_free_symbols if s not in state
})
null_pose = cm.subs(model.pose, state)
pos = cm.pos_of(null_pose)
quat = real_quat_from_matrix(cm.rot_of(null_pose))
msg = PoseMsg()
msg.position.x = pos[0]
msg.position.y = pos[1]
msg.position.z = pos[2]
msg.orientation.x = quat[0]
msg.orientation.y = quat[1]
msg.orientation.z = quat[2]
msg.orientation.w = quat[3]
te.update_state(msg, self.integrator.state)
else:
regenerate_problem = False
if align_position in self.soft_constraints:
del self.soft_constraints[align_position]
regenerate_problem = True
if align_rotation in self.soft_constraints:
del self.soft_constraints[align_rotation]
regenerate_problem = True
if regenerate_problem:
self.generate_opt_problem()
err_ik, q_ik_goal = ik_solve_one_shot(km, eef.pose, ik_goal_start,
goal_pose)
world.update_world(q_ik_goal)
visualizer.begin_draw_cycle('pre_open_world')
visualizer.draw_poses('pre_open_world', gm.eye(4), 0.2, 0.01, [
gm.subs(goal_pose, {s: 0
for s in gm.free_symbols(goal_pose)}),
gm.subs(eef.pose, q_ik_goal)
])
visualizer.draw_world('pre_open_world', world, g=0.6, b=0.6)
visualizer.render('pre_open_world')
# Build Door opening problem
grasp_err_rot = gm.norm(gm.rot_of(goal_pose - eef.pose).elements())
grasp_err_lin = gm.norm(gm.pos_of(goal_pose - eef.pose))
active_symbols = {s for s in gm.free_symbols(eef.pose) if gm.get_symbol_type(s) == gm.TYPE_POSITION}\
.union({door_position, handle_position})
controlled_symbols = {gm.DiffSymbol(s) for s in active_symbols}
controlled_values, constraints = generate_controlled_values(
km.get_constraints_by_symbols(
controlled_symbols.union(active_symbols)),
controlled_symbols)
# Static grasp goal
goal_grasp_lin = SoftConstraint(-grasp_err_lin, -grasp_err_lin,
100.0, grasp_err_lin)
goal_grasp_ang = SoftConstraint(-grasp_err_rot, -grasp_err_rot,