def test_get_ik_arm_pose(self):
robot = self.setup_robot()
can = self.setup_can()
test_env = self.setup_environment()
robot_body = OpenRAVEBody(test_env, robot.name, robot.geom)
# robot.pose = np.array([-.5, 0, 0]).reshape((3,1))
robot.pose = np.array([-0., 0, 0]).reshape((3, 1))
robot_body.set_pose(robot.pose)
dof_value_map = {
"backHeight": robot.backHeight,
"lArmPose": robot.lArmPose.flatten(),
"lGripper": robot.lGripper,
"rArmPose": robot.rArmPose.flatten(),
"rGripper": robot.rGripper
}
robot_body.set_dof(dof_value_map)
# can.pose = np.array([-0.31622543, -0.38142561, 1.19321209]).reshape((3,1))
# can.pose = np.array([0.5, -0.2, .8]).reshape((3,1))
can.pose = np.array([.5, .2, .8]).reshape((3, 1))
# can.rotation = np.array([ 0.04588155, -0.38504402, 0.19207589]).reshape((3,1))
can.rotation = np.array([-.0, -0., 0.]).reshape((3, 1))
can_body = OpenRAVEBody(test_env, can.name, can.geom)
can_body.set_pose(can.pose, can.rotation)
def test_baxter_ik(self):
env = ParamSetup.setup_env()
baxter = ParamSetup.setup_baxter()
can = ParamSetup.setup_green_can(geom=(0.02, 0.25))
baxter_body = OpenRAVEBody(env, baxter.name, baxter.geom)
can_body = OpenRAVEBody(env, can.name, can.geom)
baxter_body.set_transparency(0.5)
can_body.set_transparency(0.5)
manip = baxter_body.env_body.GetManipulator('right_arm')
robot = baxter_body.env_body
can = can_body.env_body
dof = robot.GetActiveDOFValues()
#Open the Gripper so there won't be collisions between gripper and can
dof[9], dof[-1] = 0.02, 0.02
robot.SetActiveDOFValues(dof)
iktype = IkParameterizationType.Transform6D
thetas = np.linspace(0, np.pi * 2, 10)
target_trans = OpenRAVEBody.transform_from_obj_pose([0.9, -0.23, 0.93],
[0, 0, 0])
can_body.env_body.SetTransform(target_trans)
target_trans[:3, :3] = target_trans[:3, :3].dot(
matrixFromAxisAngle([0, np.pi / 2, 0])[:3, :3])
can_trans = target_trans
body_trans = np.eye(4)
"""
To check whether baxter ik model works, uncomment the following
"""
# env.SetViewer('qtcoin')
for theta in thetas:
can_trans[:3, :3] = target_trans[:3, :3].dot(
matrixFromAxisAngle([theta, 0, 0])[:3, :3])
solution = manip.FindIKSolutions(
IkParameterization(can_trans, iktype),
IkFilterOptions.CheckEnvCollisions)
if len(solution) > 0:
print "Solution found with pose and rotation:"
print OpenRAVEBody.obj_pose_from_transform(can_trans)
else:
print "Solution not found with pose and rotation:"
print OpenRAVEBody.obj_pose_from_transform(can_trans)
for sols in solution:
dof[10:17] = sols
robot.SetActiveDOFValues(dof)
time.sleep(.2)
body_trans[:3, :3] = can_trans[:3, :3].dot(
matrixFromAxisAngle([0, -np.pi / 2, 0])[:3, :3])
can_body.env_body.SetTransform(body_trans)
self.assertTrue(
np.allclose([0.9, -0.23, 0.93],
manip.GetTransform()[:3, 3]))
def test_add_obstacle(self):
attrs = {
"geom": [],
"pose": [(3, 5)],
"_type": ["Obstacle"],
"name": ["obstacle"]
}
attr_types = {
"geom": Obstacle,
"pose": Vector2d,
"_type": str,
"name": str
}
obstacle = parameter.Object(attrs, attr_types)
env = Environment()
"""
to ensure the _add_obstacle is working, uncomment the line below to make
sure the obstacle is being created
"""
# env.SetViewer('qtcoin')
obstacle_body = OpenRAVEBody(env, obstacle.name, obstacle.geom)
obstacle_body.set_pose([2, 0])
arr = np.eye(4)
arr[0, 3] = 2
self.assertTrue(np.allclose(obstacle_body.env_body.GetTransform(),
arr))
def lazy_spawn_or_body(self, param, name, geom):
if param.openrave_body is not None:
assert geom == param.openrave_body._geom
assert self._env == param.openrave_body.env_body.GetEnv()
else:
param.openrave_body = OpenRAVEBody(self._env, name, geom)
return param.openrave_body
def sample_rp_from_ee(self, env, rs_param, ee_pose):
"""
Using Inverse Kinematics to solve all possible robot armpose and basepose that reaches this ee_pose
robot_pose: list of full robot pose with format of(basepose, backHeight, lArmPose, lGripper, rArmPose, rGripper)
"""
ee_pos, ee_rot = ee_pose[0], ee_pose[1]
base_raidus = np.linspace(-1, 1, num=BASE_SAMPLE_SIZE)
poses = list(itertools.product(base_raidus, base_raidus))
# sample
dummy_body = OpenRAVEBody(env, rs_param.name, PR2())
possible_robot_pose = []
for pos in poses:
bp = pos + ee_pos[:2]
vec = ee_pos[:2] - bp
vec = vec / np.linalg.norm(vec)
theta = math.atan2(vec[1], vec[0])
bp = np.array([bp[0], bp[1], theta])
dummy_body.set_pose(bp)
arm_poses = robot_body.ik_arm_pose(ee_pos, ee_rot)
for arm_pose in arm_poses:
possible_robot_pose.append(
(bp, ee_arm_poses[0], rs_param.lArmPose, rs_param.lGripper,
ee_arm_poses[1:], rs_param.rGripper))
import ipdb
ipdb.set_trace()
dummy_body.delete()
return possible_robot_pose
def test_add_circle(self):
attrs = {
"geom": [1],
"pose": [(3, 5)],
"_type": ["Can"],
"name": ["can0"]
}
attr_types = {
"geom": circle.GreenCircle,
"pose": Vector2d,
"_type": str,
"name": str
}
green_can = parameter.Object(attrs, attr_types)
attrs = {
"geom": [1],
"pose": [(3, 5)],
"_type": ["Can"],
"name": ["can0"]
}
attr_types = {
"geom": circle.BlueCircle,
"pose": Vector2d,
"_type": str,
"name": str
}
blue_can = parameter.Object(attrs, attr_types)
env = Environment()
"""
to ensure the _add_circle and create_cylinder is working, uncomment the
line below to make sure cylinders are being created
"""
# env.SetViewer('qtcoin')
green_body = OpenRAVEBody(env, "can0", green_can.geom)
blue_body = OpenRAVEBody(env, "can1", blue_can.geom)
green_body.set_pose([2, 0])
arr = np.eye(4)
arr[0, 3] = 2
self.assertTrue(np.allclose(green_body.env_body.GetTransform(), arr))
blue_body.set_pose(np.array([0, -1]))
arr = np.eye(4)
arr[1, 3] = -1
self.assertTrue(np.allclose(blue_body.env_body.GetTransform(), arr))
def test_ee_reachable(self):
# InGripper, Robot, Can
robot = ParamSetup.setup_baxter()
test_env = ParamSetup.setup_env()
rPose = ParamSetup.setup_baxter_pose()
ee_pose = ParamSetup.setup_ee_pose()
pred = baxter_predicates.BaxterEEReachablePos("ee_reachable", [robot, rPose, ee_pose], ["Robot", "RobotPose", "EEPose"], test_env)
pred2 = baxter_predicates.BaxterEEReachableRot("ee_reachable_rot", [robot, rPose, ee_pose], ["Robot", "RobotPose", "EEPose"], test_env)
baxter = pred._param_to_body[robot]
# Since this predicate is not yet concrete
self.assertFalse(pred.test(0))
ee_pose.value = np.array([[1.2, -0.1, 0.925]]).T
ee_pose.rotation = np.array([[0,0,0]]).T
ee_targ = ParamSetup.setup_green_can()
ee_body = OpenRAVEBody(test_env, "EE_Pose", ee_targ.geom)
ee_body.set_pose(ee_pose.value[:, 0], ee_pose.rotation[:, 0])
robot.lArmPose = np.zeros((7,7))
robot.lGripper = np.ones((1, 7))*0.02
robot.rGripper = np.ones((1, 7))*0.02
robot.pose = np.zeros((1,7))
robot.rArmPose = np.zeros((7,7))
# initialized pose value is not right
self.assertFalse(pred.test(0))
# Find IK Solution
trajectory = []
trajectory.append(baxter.get_ik_from_pose([1.2-3*const.APPROACH_DIST, -0.1, 0.925], [0,0,0], "right_arm")[0]) #s=-3
trajectory.append(baxter.get_ik_from_pose([1.2-2*const.APPROACH_DIST, -0.1, 0.925], [0,0,0], "right_arm")[0]) #s=-2
trajectory.append(baxter.get_ik_from_pose([1.2-const.APPROACH_DIST, -0.1, 0.925], [0,0,0], "right_arm")[0]) #s=-1
trajectory.append(baxter.get_ik_from_pose([1.2, -0.1, 0.925], [0,0,0], "right_arm")[0]) #s=0
trajectory.append(baxter.get_ik_from_pose([1.2, -0.1, 0.925+const.RETREAT_DIST], [0,0,0], "right_arm")[0]) #s=1
trajectory.append(baxter.get_ik_from_pose([1.2, -0.1, 0.925+2*const.RETREAT_DIST], [0,0,0], "right_arm")[0]) #s=2
trajectory.append(baxter.get_ik_from_pose([1.2, -0.1, 0.925+3*const.RETREAT_DIST], [0,0,0], "right_arm")[0]) #s=3
trajectory = np.array(trajectory)
robot.rArmPose = trajectory.T
# Predicate should succeed in the grasping post at t=3,
# EEreachableRot should always pass since rotation is right all the time
self.assertFalse(pred.test(0))
self.assertTrue(pred2.test(0))
self.assertFalse(pred.test(1))
self.assertTrue(pred2.test(1))
self.assertFalse(pred.test(2))
self.assertTrue(pred2.test(2))
self.assertTrue(pred.test(3))
self.assertTrue(pred2.test(3))
# Since finding ik introduced some error, we relax the tolerance to 1e-3
if const.TEST_GRAD: pred.expr.expr.grad(pred.get_param_vector(3), True, 1e-3)
if const.TEST_GRAD: pred2.expr.expr.grad(pred2.get_param_vector(3), True, 1e-3)
def test_ee_reachable(self):
# InGripper, Robot, Can
robot = ParamSetup.setup_pr2()
test_env = ParamSetup.setup_env()
rPose = ParamSetup.setup_pr2_pose()
ee_pose = ParamSetup.setup_ee_pose()
pred = pr2_predicates.PR2EEReachablePos("ee_reachable", [robot, rPose, ee_pose], ["Robot", "RobotPose", "EEPose"], test_env)
pred2 = pr2_predicates.PR2EEReachableRot("ee_reachable_rot", [robot, rPose, ee_pose], ["Robot", "RobotPose", "EEPose"], test_env)
pr2 = pred._param_to_body[robot]
# Since this predicate is not yet concrete
self.assertFalse(pred.test(0))
# ee_pose.value = np.array([[0.951, -0.188, 0.790675]]).T
ee_pose.value = np.array([[0.440, -0.339, 0.791]]).T
ee_pose.rotation = np.array([[0,0,0]]).T
ee_targ = ParamSetup.setup_green_can()
ee_body = OpenRAVEBody(test_env, "EE_Pose", ee_targ.geom)
ee_body.set_pose(ee_pose.value[:, 0], ee_pose.rotation[:, 0])
robot.lArmPose = np.zeros((7,7))
robot.lGripper = np.ones((1, 7))*0.528
robot.rArmPose = np.zeros((7,7))
robot.rGripper = np.ones((1, 7))*0.528
robot.pose = np.zeros((3,7))
robot.backHeight = np.zeros((1,7))
# initialized pose value is not right
self.assertFalse(pred.test(0))
# Find IK Solution
trajectory = []
trajectory.append(pr2.get_ik_from_pose([0.440-3*const.APPROACH_DIST, -0.339, 0.791], [0,0,0], "rightarm_torso")[0]) #s=-3
trajectory.append(pr2.get_ik_from_pose([0.440-2*const.APPROACH_DIST, -0.339, 0.791], [0,0,0], "rightarm_torso")[0]) #s=-2
trajectory.append(pr2.get_ik_from_pose([0.440-const.APPROACH_DIST, -0.339, 0.791], [0,0,0], "rightarm_torso")[0]) #s=-1
trajectory.append(pr2.get_ik_from_pose([0.440, -0.339, 0.791], [0,0,0], "rightarm_torso")[0]) #s=0
trajectory.append(pr2.get_ik_from_pose([0.440, -0.339, 0.791+const.RETREAT_DIST], [0,0,0], "rightarm_torso")[0]) #s=1
trajectory.append(pr2.get_ik_from_pose([0.440, -0.339, 0.791+2*const.RETREAT_DIST], [0,0,0], "rightarm_torso")[0]) #s=2
trajectory.append(pr2.get_ik_from_pose([0.440, -0.339, 0.791+3*const.RETREAT_DIST], [0,0,0], "rightarm_torso")[0]) #s=3
trajectory = np.array(trajectory).T
robot.backHeight = trajectory[[0], :]
robot.rArmPose = trajectory[1:, :]
# Predicate should succeed in the grasping post at t=3,
# EEreachableRot should always pass since rotation is right all the time
self.assertFalse(pred.test(0))
self.assertTrue(pred2.test(0))
self.assertFalse(pred.test(1))
self.assertTrue(pred2.test(1))
self.assertFalse(pred.test(2))
self.assertTrue(pred2.test(2))
self.assertTrue(pred.test(3))
self.assertTrue(pred2.test(3))
def test_closest_arm_pose(self):
env = ParamSetup.setup_env()
# env.SetViewer('qtcoin')
can = ParamSetup.setup_blue_can()
robot = ParamSetup.setup_pr2()
can.pose = np.array([[0, -.2, .8]]).T
can_body = OpenRAVEBody(env, can.name, can.geom)
can_body.set_pose(can.pose.flatten(), can.rotation.flatten())
can_body.set_transparency(.7)
robot.pose = np.array([[-.5, 0, 0]]).T
robot_body = OpenRAVEBody(env, robot.name, robot.geom)
robot_body.set_transparency(.7)
robot_body.set_pose(robot.pose.flatten())
dof_value_map = {
"backHeight": robot.backHeight,
"lArmPose": robot.lArmPose.flatten(),
"lGripper": robot.lGripper,
"rArmPose": robot.rArmPose.flatten(),
"rGripper": robot.rGripper
}
robot_body.set_dof(dof_value_map)
can_trans = OpenRAVEBody.transform_from_obj_pose(
can.pose, can.rotation)
rot_mat = matrixFromAxisAngle([0, np.pi / 2, 0])
rot_mat = can_trans[:3, :3].dot(rot_mat[:3, :3])
can_trans[:3, :3] = rot_mat
torso_pose, arm_pose = sampling.get_torso_arm_ik(
robot_body, can_trans, robot.rArmPose)
dof_value_map = {
"backHeight": robot.backHeight,
"lArmPose": robot.lArmPose.flatten(),
"lGripper": robot.lGripper,
"rArmPose": robot.rArmPose.flatten(),
"rGripper": robot.rGripper
}
robot_body.set_dof(dof_value_map)
def test_sample_ee_from_target(self):
solver = can_solver.CanSolver()
env = ParamSetup.setup_env()
# env.SetViewer('qtcoin')
target = ParamSetup.setup_target()
target.value = np.array([[0, 0, 0]]).T
target.rotation = np.array([[1.1, .3, 0]]).T
dummy_targ_geom = can.BlueCan(0.04, 0.25)
target_body = OpenRAVEBody(env, target.name, dummy_targ_geom)
target_body.set_pose(target.value.flatten(), target.rotation.flatten())
target_body.set_transparency(.7)
robot = ParamSetup.setup_pr2()
robot_body = OpenRAVEBody(env, robot.name, robot.geom)
robot_body.set_transparency(.7)
robot_body.set_pose(robot.pose.flatten())
dof_value_map = {
"backHeight": robot.backHeight,
"lArmPose": robot.lArmPose.flatten(),
"lGripper": robot.lGripper,
"rArmPose": robot.rArmPose.flatten(),
"rGripper": robot.rGripper
}
robot_body.set_dof(dof_value_map)
dummy_ee_pose_geom = can.GreenCan(.03, .3)
ee_list = list(
enumerate(
sampling.get_ee_from_target(target.value, target.rotation)))
for ee_pose in ee_list:
ee_pos, ee_rot = ee_pose[1]
body = OpenRAVEBody(env, "dummy" + str(ee_pose[0]),
dummy_ee_pose_geom)
body.set_pose(ee_pos, ee_rot)
body.set_transparency(.9)
def test_exception(self):
env = Environment()
attrs = {
"geom": [],
"pose": [(3, 5)],
"_type": ["Can"],
"name": ["can0"]
}
attr_types = {
"geom": DummyGeom,
"pose": Vector2d,
"_type": str,
"name": str
}
green_can = parameter.Object(attrs, attr_types)
with self.assertRaises(OpenRAVEException) as cm:
green_body = OpenRAVEBody(env, "can0", green_can.geom)
self.assertTrue("Geometry not supported" in cm.exception.message)
def sample_rp_from_ee(t, plan, ee_poses, robot):
target_pose = target.value[:, 0]
dummy_body = OpenRAVEBody(plan.env, robot.name + '_dummy', robot.geom)
possible_rps = []
for ee_pose in ee_poses:
base_pose = sampling.get_col_free_base_pose_around_target(
t, plan, ee_pose.value, robot).reshape((1, 3))
ik_arm_poses = dummy_body.ik_arm_pose(ee_pose[0], ee_pose[1])
cur_arm_poses = np.c_[robot.backHeight, robot.rArmPose].reshape(
(8, ))
chosen_pose = sampling.closest_arm_pose(ik_arm_poses,
cur_arm_poses)
torso_pose, arm_pose = chosen_pose[0], chosen_pose[1:]
possible_rp = np.hstack(
(base_pose, torso_pose, robot.lArmPose[:, t],
robot.lGripper[:, t], rArmPose, robot.rGripper[:, t]))
possible_rps.append(possible_rp)
dummy_body.delete()
if len(possible_rps) <= 0:
print "something went wrong"
# import ipdb; ipdb.set_trace()
return possible_rps
def test_ik_arm_pose(self):
env = Environment()
attrs = {
"name": ["pr2"],
"pose": [(0, 0, 0)],
"_type": ["Robot"],
"geom": [],
"backHeight": [0.2],
"lGripper": [0.5],
"rGripper": [0.5]
}
attrs["lArmPose"] = [(0, 0, 0, 0, 0, 0, 0)]
attrs["rArmPose"] = [(0, 0, 0, 0, 0, 0, 0)]
attr_types = {
"name": str,
"pose": matrix.Vector3d,
"_type": str,
"geom": PR2,
"backHeight": matrix.Value,
"lArmPose": matrix.Vector7d,
"rArmPose": matrix.Vector7d,
"lGripper": matrix.Value,
"rGripper": matrix.Value
}
robot = parameter.Object(attrs, attr_types)
# Set the initial arm pose so that pose is not close to joint limit
robot.lArmPose = np.array([[
np.pi / 4, np.pi / 8, np.pi / 2, -np.pi / 2, np.pi / 8, -np.pi / 8,
np.pi / 2
]]).T
robot.rArmPose = np.array([[
-np.pi / 4, np.pi / 8, -np.pi / 2, -np.pi / 2, -np.pi / 8,
-np.pi / 8, np.pi / 2
]]).T
attrs = {
"name": ["can"],
"geom": (0.04, 0.25),
"pose": ["undefined"],
"rotation": [(0, 0, 0)],
"_type": ["Can"]
}
attr_types = {
"name": str,
"geom": BlueCan,
"pose": matrix.Vector3d,
"rotation": matrix.Vector3d,
"_type": str
}
can = parameter.Object(attrs, attr_types)
can.pose = np.array([[5.77887566e-01, -1.26743678e-01,
8.37601627e-01]]).T
can.rotation = np.array([[np.pi / 4, np.pi / 4, np.pi / 4]]).T
# Create openRAVEBody for each parameter
can_body = OpenRAVEBody(env, can.name, can.geom)
robot_body = OpenRAVEBody(env, robot.name, robot.geom)
# Set the poses and dof values for each body
can_body.set_pose(can.pose, can.rotation)
robot_body.set_pose(robot.pose)
robot_body.set_dof(robot.backHeight, robot.lArmPose, robot.lGripper,
robot.rArmPose, robot.rGripper)
# Solve the IK solution
ik_arm = robot_body.ik_arm_pose(can.pose, can.rotation)[0]
robot_body.set_dof(ik_arm[:1], robot.lArmPose, robot.lGripper,
ik_arm[1:], robot.rGripper)
robot_trans = robot_body.env_body.GetLink(
"r_gripper_tool_frame").GetTransform()
robot_pos = OpenRAVEBody.obj_pose_from_transform(robot_trans)
# resulted robot eepose should be exactly the same as that can pose
self.assertTrue(
np.allclose(can.pose.flatten(), robot_pos[:3], atol=1e-4))
self.assertTrue(
np.allclose(can.rotation.flatten(), robot_pos[3:], atol=1e-4))
"""
