l_arm_cur = pr2.larm.get_joint_positions()
cmd_states["l_arm"] = mu.linspace2d(l_arm_cur,
pr2.rarm.L_POSTURES["tucked"] - l_arm_cur,
n_steps)
r_grip_cur = pr2.rgrip.get_angle()
cmd_states["r_gripper"] = np.linspace(r_grip_cur, .08 - r_grip_cur, n_steps)
l_grip_cur = pr2.lgrip.get_angle()
cmd_states["l_gripper"] = np.linspace(l_grip_cur, .08 - l_grip_cur, n_steps)
base_cur = pr2.base.get_pose("/odom_combined")
cmd_states["base"] = mu.linspace2d(base_cur, np.random.randn(3), n_steps)
trajectories.follow_body_traj(pr2,
cmd_states,
times=cmd_times,
r_arm=True,
l_arm=True,
r_gripper=True,
l_gripper=True,
base=True,
wait=False)
actual_times = []
actual_states = np.zeros(5000, trajectories.BodyState)
t_start = time()
size = 0
while time() - t_start < cmd_times[-1] + 1: #why doesn't is_moving() work???
pr2.update_rave()
actual_times.append(time() - t_start)
actual_states["r_arm"][size] = pr2.rarm.get_joint_positions()
actual_states["l_arm"][size] = pr2.larm.get_joint_positions()
cmd_times = np.linspace(0,3,n_steps)
cmd_states = np.zeros(n_steps, dtype=trajectories.BodyState)
r_arm_cur = pr2.rarm.get_joint_positions()
cmd_states["r_arm"] = mu.linspace2d(r_arm_cur, mirror_arm_joints(pr2.rarm.L_POSTURES["untucked"]) - r_arm_cur,n_steps)
l_arm_cur = pr2.larm.get_joint_positions()
cmd_states["l_arm"] = mu.linspace2d(l_arm_cur, pr2.rarm.L_POSTURES["tucked"] - l_arm_cur, n_steps)
r_grip_cur = pr2.rgrip.get_angle()
cmd_states["r_gripper"] = np.linspace(r_grip_cur, .08 - r_grip_cur, n_steps)
l_grip_cur = pr2.lgrip.get_angle()
cmd_states["l_gripper"] = np.linspace(l_grip_cur, .08 - l_grip_cur, n_steps)
base_cur = pr2.base.get_pose("/odom_combined")
cmd_states["base"] = mu.linspace2d(base_cur, np.random.randn(3), n_steps)
trajectories.follow_body_traj(pr2, cmd_states, times=cmd_times, r_arm=True, l_arm=True, r_gripper=True, l_gripper=True, base=True, wait=False)
actual_times = []
actual_states = np.zeros(5000, trajectories.BodyState)
t_start = time()
size=0
while time() - t_start < cmd_times[-1]+1: #why doesn't is_moving() work???
pr2.update_rave()
actual_times.append(time() - t_start)
actual_states["r_arm"][size] = pr2.rarm.get_joint_positions()
actual_states["l_arm"][size] = pr2.larm.get_joint_positions()
actual_states["r_gripper"][size] = pr2.rgrip.get_angle()
actual_states["l_gripper"][size] = pr2.lgrip.get_angle()
actual_states["base"][size] = pr2.base.get_pose("/odom_combined")
def callback(req):
Globals.handles = []
traj = req.traj
if traj.arms_used in "rl":
lr = traj.arms_used
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
manip = Globals.pr2.robot.GetManipulator(manip_name)
gripper0_xyzs, gripper0_quats = [], []
for pose in traj.gripper0_poses.poses:
xyz, quat = conversions.pose_to_trans_rot(pose)
gripper0_xyzs.append(xyz)
gripper0_quats.append(quat)
joint_positions, inds = trajectories.make_joint_traj(
gripper0_xyzs, gripper0_quats, manip, "base_footprint", "%s_gripper_tool_frame" % lr, filter_options=1
)
feasible_frac = len(inds) / len(gripper0_xyzs)
best_feasible_frac, best_lr, best_joint_positions = feasible_frac, lr, joint_positions
if best_feasible_frac < 0.7:
rospy.logerr("unacceptably low fraction of ik success: %.4f", best_feasible_frac)
return ExecTrajectoryResponse(success=False)
rospy.loginfo("using %s hand", {"l": "left", "r": "right"}[best_lr])
gripper_angles = np.array(traj.gripper0_angles)
rospy.logwarn("warning! gripper angle hack")
gripper_angles[gripper_angles < 0.04] = gripper_angles[gripper_angles < 0.04] - 0.02
body_traj = np.zeros(len(best_joint_positions), dtype=trajectories.BodyState)
body_traj["%s_arm" % best_lr] = best_joint_positions
body_traj["%s_gripper" % best_lr] = gripper_angles
pose_array = gm.PoseArray()
pose_array.header.frame_id = "base_footprint"
pose_array.poses = traj.gripper0_poses.poses
Globals.handles.append(Globals.rviz.draw_curve(pose_array, rgba=(1, 0, 0, 1)))
trajectories.follow_body_traj(
Globals.pr2,
body_traj,
times=None,
r_arm=(best_lr == "r"),
r_gripper=(best_lr == "r"),
l_arm=(best_lr == "l"),
l_gripper=(best_lr == "l"),
)
elif traj.arms_used in "b":
body_traj = np.zeros(len(traj.gripper0_poses.poses), dtype=trajectories.BodyState)
for (lr, gripper_poses, gripper_angles) in zip(
"lr", [traj.gripper0_poses.poses, traj.gripper1_poses.poses], [traj.gripper0_angles, traj.gripper1_angles]
):
gripper_angles = np.array(gripper_angles)
rospy.logwarn("warning! gripper angle hack")
gripper_angles[gripper_angles < 0.04] = gripper_angles[gripper_angles < 0.04] - 0.02
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
manip = Globals.pr2.robot.GetManipulator(manip_name)
gripper_xyzs, gripper_quats = [], []
for pose in gripper_poses:
xyz, quat = conversions.pose_to_trans_rot(pose)
gripper_xyzs.append(xyz)
gripper_quats.append(quat)
joint_positions, inds = trajectories.make_joint_traj(
gripper_xyzs, gripper_quats, manip, "base_footprint", "%s_gripper_tool_frame" % lr, filter_options=1
)
if len(inds) == 0:
return ExecTrajectoryResponse(success=False)
feasible_frac = len(inds) / len(gripper_xyzs)
body_traj["%s_arm" % lr] = joint_positions
body_traj["%s_gripper" % lr] = gripper_angles
pose_array = gm.PoseArray()
pose_array.header.frame_id = "base_footprint"
pose_array.header.stamp = rospy.Time.now()
pose_array.poses = traj.gripper0_poses.poses
Globals.handles.append(Globals.rviz.draw_curve(pose_array, rgba=(1, 0, 0, 1)))
trajectories.follow_body_traj(
Globals.pr2, body_traj, times=None, r_arm=True, r_gripper=True, l_arm=True, l_gripper=True
)
else:
raise NotImplementedError
Globals.pr2.join_all()
return ExecTrajectoryResponse(success=True)
def execute(self, userdata):
Globals.pr2.update_rave()
trajectories.follow_body_traj(Globals.pr2, userdata.trajectory, times=None,
l_arm = userdata.left_used, r_arm = userdata.right_used,
l_gripper = userdata.left_used, r_gripper = userdata.right_used)
return "success"
def callback(request):
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
#Globals.rviz.remove_all_markers()
#draw_table()
new_cloud1, _ = pc2xyzrgb(request.object_clouds[0])
new_cloud2, _ = pc2xyzrgb(request.object_clouds[1])
new_cloud1, new_cloud2 = sorted([new_cloud1, new_cloud2], key = lambda x: np.squeeze(x).ptp(axis=0).prod())
new_cloud1 = new_cloud1.reshape(-1,3)
new_cloud2 = new_cloud2.reshape(-1,3)
new_xyz1 = (new_cloud1.max(axis=0) + new_cloud1.min(axis=0))/2
new_xyz2 = (new_cloud2.max(axis=0) + new_cloud2.min(axis=0))/2
f.fit(np.array([xyz1, xyz2]), np.array([new_xyz1, new_xyz2]), 1e6, 1e-3)
new_gripper_xyzs, new_gripper_mats = f.transform_frames(old_gripper_xyzs, conversions.quats2mats(old_gripper_quats))
new_gripper_quats = conversions.mats2quats(new_gripper_mats)
#print "warning: using old oreitnations"
show_objects_and_trajectory(new_gripper_xyzs, np.array([new_xyz1, new_xyz2]), np.array([quat1, quat2]), obj_dims, (0,1,0,1))
show_objects_and_trajectory(old_gripper_xyzs, np.array([xyz1, xyz2]), np.array([quat1, quat2]), obj_dims, (0,0,1,1))
grid_handle = draw_grid(Globals.rviz, f.transform_points, old_gripper_xyzs.min(axis=0), old_gripper_xyzs.max(axis=0), "base_footprint")
HANDLES.append(grid_handle)
Globals.pr2.join_all()
Globals.pr2.update_rave()
best_traj_info, best_feasible_frac = None, 0
env = Globals.pr2.robot.GetEnv()
Globals.pr2.update_rave()
collision_env = create_obstacle_env(env)
basemanip = openravepy.interfaces.BaseManipulation(collision_env.GetRobot("pr2"),plannername=None)
rospy.sleep(.1)
#collision_env.SetViewer("qtcoin")
#raw_input("press enter to continue")
for (lr, arm) in zip("lr",[Globals.pr2.larm,Globals.pr2.rarm]):
name = arm.manip.GetName()
manip = collision_env.GetRobot('pr2').GetManipulator(name)
rospy.loginfo("trying to plan to initial position with %s",name)
first_mat1 = np.eye(4)
first_mat1[:3,:3] = new_gripper_mats[0]
first_mat1[:3,3] = new_gripper_xyzs[0]
first_mat = transform_relative_pose_for_ik(manip, first_mat1, "world", "%s_gripper_tool_frame"%lr)
collision_env.GetRobot("pr2").SetActiveManipulator(name)
trajobj = None
try:
trajobj = basemanip.MoveToHandPosition([first_mat],seedik=16,outputtrajobj=True,execute=0)
rospy.loginfo("planning succeeded")
except Exception:
rospy.loginfo("planning failed")
traceback.print_exc()
print "initial ik result", manip.FindIKSolutions(first_mat,0)
continue
rospy.loginfo("trying ik")
Globals.pr2.update_rave()
joint_positions, inds = trajectories.make_joint_traj(new_gripper_xyzs, new_gripper_quats, manip, 'base_footprint', '%s_gripper_tool_frame'%lr, filter_options = 1+16)
feasible_frac = len(inds)/len(new_gripper_xyzs)
print inds
if feasible_frac > best_feasible_frac:
best_feasible_frac = feasible_frac
best_traj_info = dict(
feasible_frac = feasible_frac,
lr = 'l' if name == 'leftarm' else 'r',
manip = manip,
arm = arm,
initial_traj = trajobj,
joint_positions = joint_positions)
collision_env.Destroy()
response = PourResponse()
if best_feasible_frac > .75:
if best_traj_info["initial_traj"] is not None:
follow_rave_traj(best_traj_info["arm"], best_traj_info["initial_traj"])
else:
print "no initial traj"
#print "feasible inds", best_traj_info["inds"]
body_traj = np.zeros(len(best_traj_info["joint_positions"]),dtype=trajectories.BodyState)
lr = best_traj_info["lr"]
body_traj["%s_arm"%lr] = best_traj_info["joint_positions"]
body_traj["%s_gripper"%lr] = gripper_angles
trajectories.follow_body_traj(Globals.pr2, body_traj, times,
r_arm = (lr=='r'), r_gripper = (lr=='r'), l_arm = (lr=='l'), l_gripper= (lr=='l'))
Globals.pr2.join_all()
response.success = True
else:
rospy.logerr("could not execute trajectory because not enough points are reachable")
response.success = False
return response
