def execute(self,userdata): """ - lookup closest trajectory from database - if it's a terminal state, we're done - warp it based on the current rope returns: done, not_done, failure """ xyz_new = np.squeeze(np.asarray(userdata.points)) #if args.obj == "cloth": xyz_new = voxel_downsample(xyz_new, .025) xyz_new_ds, ds_inds = downsample(xyz_new) dists_new = recognition.calc_geodesic_distances_downsampled_old(xyz_new,xyz_new_ds, ds_inds) if args.human_select_demo: raise NotImplementedError seg_name = trajectory_library.interactive_select_demo(demos) best_demo = demos[seg_name] pts0,_ = best_demo["cloud_xyz_ds"] pts1,_ = downsample(xyz_new) self.f = registration.tps_rpm(pts0, pts1, plotting = 4, reg_init=1,reg_final=args.reg_final,n_iter=40) else: if args.count_steps: candidate_demo_names = self.count2segnames[Globals.stage] else: candidate_demo_names = demos.keys() from joblib import parallel costs_names = parallel.Parallel(n_jobs=-2)(parallel.delayed(calc_seg_cost)(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names) #costs_names = [calc_seg_cost(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names] #costs_names = [calc_seg_cost(seg_name) for seg_name in candidate_demo_names] _, best_name = min(costs_names) best_demo = demos[best_name] if best_demo["done"]: rospy.loginfo("best demo was a 'done' state") return "done" best_demo = demos[best_name] rospy.loginfo("best segment name: %s", best_name) xyz_demo_ds = best_demo["cloud_xyz_ds"] if args.test: n_iter = 21 else: n_iter = 101 if args.use_rigid: self.f = registration.Translation2d() self.f.fit(xyz_demo_ds, xyz_new_ds) else: self.f = registration.tps_rpm(xyz_demo_ds, xyz_new_ds, plotting = 20, reg_init=1,reg_final=.01,n_iter=n_iter,verbose=False)#, interactive=True) np.savez('registration_data', xyz_demo_ds=xyz_demo_ds, xyz_new_ds=xyz_new_ds) # print 'correspondences', self.f.corr_nm #################### Generate new trajectory ################## #### Plot original and warped point clouds ####### # orig_pose_array = conversions.array_to_pose_array(np.squeeze(best_demo["cloud_xyz_ds"]), "base_footprint") # warped_pose_array = conversions.array_to_pose_array(self.f.transform_points(np.squeeze(best_demo["cloud_xyz_ds"])), "base_footprint") # Globals.handles.append(Globals.rviz.draw_curve(orig_pose_array,rgba=(1,0,0,1),id=19024,type=Marker.CUBE_LIST)) # Globals.handles.append(Globals.rviz.draw_curve(warped_pose_array,rgba=(0,1,0,1),id=2983,type=Marker.CUBE_LIST)) #### Plot grid ######## mins = np.squeeze(best_demo["cloud_xyz"]).min(axis=0) maxes = np.squeeze(best_demo["cloud_xyz"]).max(axis=0) mins[2] -= .1 maxes[2] += .1 grid_handle = warping.draw_grid(Globals.rviz, self.f.transform_points, mins, maxes, 'base_footprint') Globals.handles.append(grid_handle) #### Actually generate the trajectory ########### warped_demo = warping.transform_demo_with_fingertips(self.f, best_demo) if yes_or_no('dump warped demo?'): import pickle fname = '/tmp/warped_demo_' + str(np.random.randint(9999999999)) + '.pkl' with open(fname, 'w') as f: pickle.dump(warped_demo, f) print 'saved to', fname Globals.pr2.update_rave() trajectory = {} # calculate joint trajectory using IK for lr in "lr": leftright = {"l":"left","r":"right"}[lr] if best_demo["arms_used"] in [lr, "b"]: if args.hard_table: clipinplace(warped_demo["l_gripper_tool_frame"]["position"][:,2],Globals.table_height+.032,np.inf) clipinplace(warped_demo["r_gripper_tool_frame"]["position"][:,2],Globals.table_height+.032,np.inf) arm_traj, feas_inds = lfd_traj.make_joint_traj_by_graph_search( warped_demo["%s_gripper_tool_frame"%lr]["position"], warped_demo["%s_gripper_tool_frame"%lr]["orientation"], Globals.pr2.robot.GetManipulator("%sarm"%leftright), "%s_gripper_tool_frame"%lr, check_collisions=True ) if len(feas_inds) == 0: return "failure" trajectory["%s_arm"%lr] = arm_traj trajectory["%s_grab"%lr] = best_demo["%s_gripper_joint"%lr] < .07 trajectory["%s_gripper"%lr] = warped_demo["%s_gripper_joint"%lr] trajectory["%s_gripper"%lr][trajectory["%s_grab"%lr]] = 0 # smooth any discontinuities in the arm traj for lr in "lr": leftright = {"l":"left","r":"right"}[lr] if best_demo["arms_used"] in [lr, "b"]: trajectory["%s_arm"%lr], discont_times, n_steps = lfd_traj.smooth_disconts( trajectory["%s_arm"%lr], Globals.pr2.env, Globals.pr2.robot.GetManipulator("%sarm"%leftright), "%s_gripper_tool_frame"%lr ) # after smoothing the arm traj, we need to fill in all other trajectories (in both arms) other_lr = 'r' if lr == 'l' else 'l' if best_demo["arms_used"] in [other_lr, "b"]: trajectory["%s_arm"%other_lr] = lfd_traj.fill_stationary(trajectory["%s_arm"%other_lr], discont_times, n_steps) for tmp_lr in 'lr': if best_demo["arms_used"] in [tmp_lr, "b"]: trajectory["%s_grab"%tmp_lr] = lfd_traj.fill_stationary(trajectory["%s_grab"%tmp_lr], discont_times, n_steps) trajectory["%s_gripper"%tmp_lr] = lfd_traj.fill_stationary(trajectory["%s_gripper"%tmp_lr], discont_times, n_steps) trajectory["%s_gripper"%tmp_lr][trajectory["%s_grab"%tmp_lr]] = 0 # plotting for lr in "lr": leftright = {"l":"left","r":"right"}[lr] if best_demo["arms_used"] in [lr, "b"]: # plot warped trajectory Globals.handles.append(Globals.rviz.draw_curve( conversions.array_to_pose_array( alternate(warped_demo["%s_gripper_l_finger_tip_link"%lr]["position"], warped_demo["%s_gripper_r_finger_tip_link"%lr]["position"]), "base_footprint" ), width=.001, rgba = (1,0,1,.4), type=Marker.LINE_LIST, ns='warped_finger_traj' )) # plot original trajectory Globals.handles.append(Globals.rviz.draw_curve( conversions.array_to_pose_array( alternate(best_demo["%s_gripper_l_finger_tip_link"%lr]["position"], best_demo["%s_gripper_r_finger_tip_link"%lr]["position"]), "base_footprint" ), width=.001, rgba = (0,1,1,.4), type=Marker.LINE_LIST, ns='demo_finger_traj' )) userdata.trajectory = trajectory if args.prompt_before_motion: consent = yes_or_no("trajectory ok?") else: consent = True if consent: return "not_done" else: return "failure"
def execute(self,userdata): """ - lookup closest trajectory from database - if it's a terminal state, we're done - warp it based on the current rope returns: done, not_done, failure visualization: - show all library states - warping visualization from matlab """ xyz_new = np.squeeze(np.asarray(userdata.points)) if args.obj == "cloth": xyz_new = voxel_downsample(xyz_new, .025) xyz_new_ds, ds_inds = downsample(xyz_new) dists_new = recognition.calc_geodesic_distances_downsampled(xyz_new,xyz_new_ds, ds_inds) if HUMAN_SELECT_DEMO: seg_name = trajectory_library.interactive_select_demo(self.library) best_demo = self.library.root[seg_name] pts0,_ = downsample(np.asarray(best_demo["cloud_xyz"])) pts1,_ = downsample(xyz_new) self.f = registration.tps_rpm(pts0, pts1, plotting = 4, reg_init=1,reg_final=.025,n_iter=40) else: best_f = None best_cost = np.inf best_name = None for (seg_name,candidate_demo) in self.library.root.items(): xyz_demo = np.squeeze(np.asarray(candidate_demo["cloud_xyz"])) if args.obj == "cloth": xyz_demo = voxel_downsample(xyz_demo, .025) xyz_demo_ds, ds_inds = downsample(xyz_demo)#voxel_downsample(xyz_demo, DS_LENGTH, return_inds = True) dists_demo = recognition.calc_geodesic_distances_downsampled(xyz_demo, xyz_demo_ds, ds_inds) cost = recognition.calc_match_score(xyz_new_ds, xyz_demo_ds, dists0 = dists_new, dists1 = dists_demo) print "seg_name: %s. cost: %s"%(seg_name, cost) if cost < best_cost: best_cost = cost best_name = seg_name #if best_name.startswith("done"): return "done" best_demo = self.library.root[best_name] xyz_demo_ds,_ = downsample(np.asarray(best_demo["cloud_xyz"][0])) self.f = registration.tps_rpm(xyz_demo_ds, xyz_new_ds, plotting = 10, reg_init=1,reg_final=.01,n_iter=200,verbose=True) print "best segment:", best_name orig_pose_array = conversions.array_to_pose_array(best_demo["cloud_xyz"][0], "base_footprint") warped_pose_array = conversions.array_to_pose_array(self.f.transform_points(best_demo["cloud_xyz"][0]), "base_footprint") HANDLES.append(Globals.rviz.draw_curve(orig_pose_array,rgba=(1,0,0,1),id=19024,type=Marker.CUBE_LIST)) HANDLES.append(Globals.rviz.draw_curve(warped_pose_array,rgba=(0,1,0,1),id=2983,type=Marker.CUBE_LIST)) mins = best_demo["cloud_xyz"][0].min(axis=0) maxes = best_demo["cloud_xyz"][0].max(axis=0) mins[2] -= .1 maxes[2] += .1 grid_handle = warping.draw_grid(Globals.rviz, self.f.transform_points, mins, maxes, 'base_footprint') HANDLES.append(grid_handle) #self.f = fit_tps(demo["rope"][0], userdata.points) userdata.left_used = left_used = best_demo["arms_used"].value in "lb" userdata.right_used = right_used = best_demo["arms_used"].value in "rb" print "left_used", left_used print "right_used", right_used warped_demo = warping.transform_demo_with_fingertips(self.f, best_demo, left=left_used, right=right_used) trajectory = np.zeros(len(best_demo["times"]), dtype=trajectories.BodyState) Globals.pr2.update_rave() if left_used: l_arm_traj, feas_inds = trajectories.make_joint_traj(warped_demo["l_gripper_xyzs"], warped_demo["l_gripper_quats"], Globals.pr2.robot.GetManipulator("leftarm"),"base_footprint","l_gripper_tool_frame",1+16) if len(feas_inds) == 0: return "failure" trajectory["l_arm"] = l_arm_traj rospy.loginfo("left arm: %i of %i points feasible", len(feas_inds), len(trajectory)) trajectory["l_gripper"] = fix_gripper(warped_demo["l_gripper_angles"]) HANDLES.append(Globals.rviz.draw_curve( conversions.array_to_pose_array( alternate(warped_demo["l_gripper_xyzs1"],warped_demo["l_gripper_xyzs2"]), "base_footprint"), width=.001, rgba = (1,0,1,.4),type=Marker.LINE_LIST)) if right_used: r_arm_traj,feas_inds = trajectories.make_joint_traj(warped_demo["r_gripper_xyzs"], warped_demo["r_gripper_quats"], Globals.pr2.robot.GetManipulator("rightarm"),"base_footprint","r_gripper_tool_frame",1+16) if len(feas_inds) == 0: return "failure" trajectory["r_arm"] = r_arm_traj rospy.loginfo("right arm: %i of %i points feasible", len(feas_inds), len(trajectory)) trajectory["r_gripper"] = fix_gripper(warped_demo["r_gripper_angles"]) HANDLES.append(Globals.rviz.draw_curve( conversions.array_to_pose_array( alternate(warped_demo["l_gripper_xyzs1"],warped_demo["l_gripper_xyzs2"]), "base_footprint"), width=.001, rgba = (1,0,1,.4),type=Marker.LINE_LIST)) userdata.trajectory = trajectory #userdata.base_xya = (x,y,0) # todo: draw pr2 # consent = yes_or_no("trajectory ok?") consent = True if consent: return "not_done" else: return "failure"
from lfd import trajectory_library as tl library = tl.TrajectoryLibrary("lm.h5", "read") tl.interactive_select_demo(library)
def execute(self, userdata): """ - lookup closest trajectory from database - if it's a terminal state, we're done - warp it based on the current rope returns: done, not_done, failure """ xyz_new = np.squeeze(np.asarray(userdata.points)) #if args.obj == "cloth": xyz_new = voxel_downsample(xyz_new, .025) xyz_new_ds, ds_inds = downsample(xyz_new) dists_new = recognition.calc_geodesic_distances_downsampled_old( xyz_new, xyz_new_ds, ds_inds) if args.human_select_demo: raise NotImplementedError seg_name = trajectory_library.interactive_select_demo(demos) best_demo = demos[seg_name] pts0, _ = best_demo["cloud_xyz_ds"] pts1, _ = downsample(xyz_new) self.f = registration.tps_rpm(pts0, pts1, plotting=4, reg_init=1, reg_final=args.reg_final, n_iter=40) else: if args.count_steps: candidate_demo_names = self.count2segnames[Globals.stage] else: candidate_demo_names = demos.keys() from joblib import parallel costs_names = parallel.Parallel(n_jobs=-2)( parallel.delayed(calc_seg_cost)(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names) #costs_names = [calc_seg_cost(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names] #costs_names = [calc_seg_cost(seg_name) for seg_name in candidate_demo_names] _, best_name = min(costs_names) best_demo = demos[best_name] if best_demo["done"]: rospy.loginfo("best demo was a 'done' state") return "done" best_demo = demos[best_name] rospy.loginfo("best segment name: %s", best_name) xyz_demo_ds = best_demo["cloud_xyz_ds"] if args.test: n_iter = 21 else: n_iter = 101 if args.use_rigid: self.f = registration.Translation2d() self.f.fit(xyz_demo_ds, xyz_new_ds) else: self.f = registration.tps_rpm(xyz_demo_ds, xyz_new_ds, plotting=20, reg_init=1, reg_final=.01, n_iter=n_iter, verbose=False) #, interactive=True) np.savez('registration_data', xyz_demo_ds=xyz_demo_ds, xyz_new_ds=xyz_new_ds) # print 'correspondences', self.f.corr_nm #################### Generate new trajectory ################## #### Plot original and warped point clouds ####### # orig_pose_array = conversions.array_to_pose_array(np.squeeze(best_demo["cloud_xyz_ds"]), "base_footprint") # warped_pose_array = conversions.array_to_pose_array(self.f.transform_points(np.squeeze(best_demo["cloud_xyz_ds"])), "base_footprint") # Globals.handles.append(Globals.rviz.draw_curve(orig_pose_array,rgba=(1,0,0,1),id=19024,type=Marker.CUBE_LIST)) # Globals.handles.append(Globals.rviz.draw_curve(warped_pose_array,rgba=(0,1,0,1),id=2983,type=Marker.CUBE_LIST)) #### Plot grid ######## mins = np.squeeze(best_demo["cloud_xyz"]).min(axis=0) maxes = np.squeeze(best_demo["cloud_xyz"]).max(axis=0) mins[2] -= .1 maxes[2] += .1 grid_handle = warping.draw_grid(Globals.rviz, self.f.transform_points, mins, maxes, 'base_footprint') Globals.handles.append(grid_handle) #### Actually generate the trajectory ########### warped_demo = warping.transform_demo_with_fingertips(self.f, best_demo) if yes_or_no('dump warped demo?'): import pickle fname = '/tmp/warped_demo_' + str( np.random.randint(9999999999)) + '.pkl' with open(fname, 'w') as f: pickle.dump(warped_demo, f) print 'saved to', fname Globals.pr2.update_rave() trajectory = {} # calculate joint trajectory using IK for lr in "lr": leftright = {"l": "left", "r": "right"}[lr] if best_demo["arms_used"] in [lr, "b"]: if args.hard_table: clipinplace( warped_demo["l_gripper_tool_frame"]["position"][:, 2], Globals.table_height + .032, np.inf) clipinplace( warped_demo["r_gripper_tool_frame"]["position"][:, 2], Globals.table_height + .032, np.inf) arm_traj, feas_inds = lfd_traj.make_joint_traj_by_graph_search( warped_demo["%s_gripper_tool_frame" % lr]["position"], warped_demo["%s_gripper_tool_frame" % lr]["orientation"], Globals.pr2.robot.GetManipulator("%sarm" % leftright), "%s_gripper_tool_frame" % lr, check_collisions=True) if len(feas_inds) == 0: return "failure" trajectory["%s_arm" % lr] = arm_traj trajectory["%s_grab" % lr] = best_demo["%s_gripper_joint" % lr] < .07 trajectory["%s_gripper" % lr] = warped_demo["%s_gripper_joint" % lr] trajectory["%s_gripper" % lr][trajectory["%s_grab" % lr]] = 0 # smooth any discontinuities in the arm traj for lr in "lr": leftright = {"l": "left", "r": "right"}[lr] if best_demo["arms_used"] in [lr, "b"]: trajectory[ "%s_arm" % lr], discont_times, n_steps = lfd_traj.smooth_disconts( trajectory["%s_arm" % lr], Globals.pr2.env, Globals.pr2.robot.GetManipulator("%sarm" % leftright), "%s_gripper_tool_frame" % lr) # after smoothing the arm traj, we need to fill in all other trajectories (in both arms) other_lr = 'r' if lr == 'l' else 'l' if best_demo["arms_used"] in [other_lr, "b"]: trajectory["%s_arm" % other_lr] = lfd_traj.fill_stationary( trajectory["%s_arm" % other_lr], discont_times, n_steps) for tmp_lr in 'lr': if best_demo["arms_used"] in [tmp_lr, "b"]: trajectory["%s_grab" % tmp_lr] = lfd_traj.fill_stationary( trajectory["%s_grab" % tmp_lr], discont_times, n_steps) trajectory["%s_gripper" % tmp_lr] = lfd_traj.fill_stationary( trajectory["%s_gripper" % tmp_lr], discont_times, n_steps) trajectory["%s_gripper" % tmp_lr][trajectory["%s_grab" % tmp_lr]] = 0 # plotting for lr in "lr": leftright = {"l": "left", "r": "right"}[lr] if best_demo["arms_used"] in [lr, "b"]: # plot warped trajectory Globals.handles.append( Globals.rviz.draw_curve(conversions.array_to_pose_array( alternate( warped_demo["%s_gripper_l_finger_tip_link" % lr]["position"], warped_demo["%s_gripper_r_finger_tip_link" % lr]["position"]), "base_footprint"), width=.001, rgba=(1, 0, 1, .4), type=Marker.LINE_LIST, ns='warped_finger_traj')) # plot original trajectory Globals.handles.append( Globals.rviz.draw_curve(conversions.array_to_pose_array( alternate( best_demo["%s_gripper_l_finger_tip_link" % lr]["position"], best_demo["%s_gripper_r_finger_tip_link" % lr]["position"]), "base_footprint"), width=.001, rgba=(0, 1, 1, .4), type=Marker.LINE_LIST, ns='demo_finger_traj')) userdata.trajectory = trajectory if args.prompt_before_motion: consent = yes_or_no("trajectory ok?") else: consent = True if consent: return "not_done" else: return "failure"