def tpsrpm_plot_cb(x_nd, y_md, targ_Nd, corr_nm, wt_n, f):
ypred_nd = f.transform_points(x_nd)
handles = []
handles.append(Globals.env.plot3(ypred_nd, 3, (0,1,0,1)))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, x_nd.min(axis=0), x_nd.max(axis=0), xres = .1, yres = .1, zres = .04))
if Globals.viewer:
Globals.viewer.Step()
def tpsrpm_plot_cb(x_nd, y_md, targ_Nd, corr_nm, wt_n, f):
ypred_nd = f.transform_points(x_nd)
handles = []
handles.append(Globals.env.plot3(ypred_nd, 3, (0, 1, 0)))
handles.extend(
plotting_openrave.draw_grid(Globals.env,
f.transform_points,
x_nd.min(axis=0),
x_nd.max(axis=0),
xres=.1,
yres=.1,
zres=.04))
Globals.viewer.Step()
def find_closest_human(demofile, new_xyz):
"for now, just prompt the user"
seg_names = demofile.keys()
print "choose from the following options (type an integer)"
for (i, seg_name) in enumerate(seg_names):
print "%i: %s" % (i, seg_name)
while True:
try:
choice_ind = int(raw_input())
chosen_seg = seg_names[choice_ind]
seg_info = demofile[chosen_seg]
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles = []
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0, 1)))
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1, 1)))
f = registration.tps_rpm(old_xyz, new_xyz, plot_cb=tpsrpm_plot_cb, plotting=1)
handles.extend(
plotting_openrave.draw_grid(
Globals.env,
f.transform_points,
old_xyz.min(axis=0),
old_xyz.max(axis=0),
xres=0.1,
yres=0.1,
zres=0.04,
)
)
inv_f = registration.tps_rpm(new_xyz, old_xyz)
K = -ssd.squareform(ssd.pdist(f.x_na))
w = f.w_ng
inv_tps_cost = np.trace(w.T.dot(K).dot(w))
K = -ssd.squareform(ssd.pdist(inv_f.x_na))
w = inv_f.w_ng
tps_cost = np.trace(w.T.dot(K).dot(w))
print "Inverse TPS Cost: %f" % (inv_tps_cost)
print "TPS Cost: %f" % (tps_cost)
response = raw_input()
break
except (IndexError, ValueError):
print "invalid selection. try again"
# print "confirm selection (y/n)"
# response = raw_input()
# if response == "y":
# break
# print "choose from the following options (type an integer)"
# for (i, seg_name) in enumerate(seg_names):
# print "%i: %s"%(i,seg_name)
return f, chosen_seg
def find_closest_human(demofile, new_xyz):
"for now, just prompt the user"
seg_names = demofile.keys()
print "choose from the following options (type an integer)"
for (i, seg_name) in enumerate(seg_names):
print "%i: %s" % (i, seg_name)
while True:
try:
choice_ind = int(raw_input())
chosen_seg = seg_names[choice_ind]
seg_info = demofile[chosen_seg]
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles = []
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0, 1)))
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1, 1)))
f = registration.tps_rpm(old_xyz,
new_xyz,
plot_cb=tpsrpm_plot_cb,
plotting=1)
handles.extend(
plotting_openrave.draw_grid(Globals.env,
f.transform_points,
old_xyz.min(axis=0),
old_xyz.max(axis=0),
xres=.1,
yres=.1,
zres=.04))
inv_f = registration.tps_rpm(new_xyz, old_xyz)
K = -ssd.squareform(ssd.pdist(f.x_na))
w = f.w_ng
inv_tps_cost = np.trace(w.T.dot(K).dot(w))
K = -ssd.squareform(ssd.pdist(inv_f.x_na))
w = inv_f.w_ng
tps_cost = np.trace(w.T.dot(K).dot(w))
print "Inverse TPS Cost: %f" % (inv_tps_cost)
print "TPS Cost: %f" % (tps_cost)
response = raw_input()
break
except (IndexError, ValueError):
print "invalid selection. try again"
#print "confirm selection (y/n)"
#response = raw_input()
#if response == "y":
# break
#print "choose from the following options (type an integer)"
#for (i, seg_name) in enumerate(seg_names):
# print "%i: %s"%(i,seg_name)
return f, chosen_seg
def tpsrpm_plot_cb(x_nd, y_md, targ_Nd, corr_nm, wt_n, f, old_xyz, new_xyz, last_one=False):
_, src_params = registration.unit_boxify(old_xyz)
_, targ_params = registration.unit_boxify(new_xyz)
f = registration.unscale_tps(f, src_params, targ_params)
#ypred_nd = f.transform_points(x_nd)
handles = []
#handles.append(Globals.env.plot3(ypred_nd, 3, (0, 1, 0, 1)))
ypred_nd = f.transform_points(old_xyz)
handles.append(Globals.env.plot3(ypred_nd, 3, (0, 1, 0, 1)))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
old_xyz.max(axis=0) + np.r_[0, 0, .1], xres=.1, yres=.1, zres=.04))
if Globals.viewer:
Globals.viewer.Step()
time.sleep(0.1)
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
else:
Globals.pr2.head.set_pan_tilt(0,1.2)
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
Globals.pr2.join_all()
time.sleep(.5)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz,5, (1,0,0)))
handles.append(Globals.env.plot3(new_xyz,5, (0,0,1)))
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
if args.execution: Globals.pr2.update_rave()
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
success &= set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
# Doesn't actually check if grab occurred, unfortunately
if not success: break
if len(bodypart2traj) > 0:
success &= exec_traj_maybesim(bodypart2traj)
if not success: break
redprint("Segment %s result: %s"%(seg_name, success))
if args.fake_data_segment: break
def main():
import IPython
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.eval.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.eval.fake_data_segment:
fake_seg = demofile[args.eval.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.eval.fake_data_transform[3:6])
hmat[:3,3] = args.eval.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
else:
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
print "got new xyz"
redprint(new_xyz)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
### Old end effector forces at r_gripper_tool_frame (including torques)
old_forces = seg_info['end_effector_forces'][:,0:3,:]
old_torques = seg_info['end_effector_forces'][:,3:6,:]
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
old_xyz_transformed = f.transform_points(old_xyz)
#handles.append(Globals.env.plot3(old_xyz_transformed,5, np.array([(0,0,1,1) for i in old_xyz_transformed])))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'r':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#print old_ee_traj
old_ee_pos = old_ee_traj[:,0:3,3]
#print old_ee_pos
# End effector forces as oppossed to end effector trajectories
dfdxs = f.compute_jacobian(old_ee_pos)
old_eefs = []
new_eefs = []
for i in xrange(len(old_forces)):
dfdx = np.matrix(dfdxs[i])
old_force = np.matrix(old_forces[i])
old_torque = np.matrix(old_torques[i])
new_force = (dfdx * old_force)
new_torque = (dfdx * old_torque)
old_eefs.append(np.vstack((old_force, old_torque)))
new_eefs.append(np.vstack((new_force, new_torque)))
old_eefs = np.asarray(old_eefs)[:,:,0]
new_eefs = np.asarray(new_eefs)[:,:,0]
force_data = {}
force_data['old_eefs'] = old_eefs
force_data['new_eefs'] = new_eefs
force_file = open("trial.pickle", 'wa')
pickle.dump(force_data, force_file)
force_file.close()
new_ee_traj_x = new_ee_traj
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'r':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
#if arm_moved(old_joint_traj): NOT SURE WHY BUT THIS IS RETURNING FALSE
lr2oldtraj[lr] = old_joint_traj
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, lr2oldtraj['r'].shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
# Setting robot arm to joint trajectory
r_vals = lr2oldtraj['r'][i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
#IPython.embed()
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
# FIRST RESAMPLING HAPPENS HERE: JOINT_LENGTH
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP) # Timesteps only, can use to inter eefs for first time
####
new_eefs_segment = asarray(new_eefs[i_start:i_end+1,:]) # Extract miniseg, and re-sample
if args.no_ds:
new_eefs_segment_rs = new_eefs_segment
else:
new_eefs_segment_rs = mu.interp2d(timesteps_rs, np.arange(len(new_eefs_segment)), new_eefs_segment)
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
if args.no_ds:
old_joint_traj_rs = old_joint_traj
new_ee_traj_rs = new_ee_traj
ds_inds = np.arange(0, len(new_ee_traj_rs), args.trajopt_ds)
new_ee_traj_rs = new_ee_traj_rs[ds_inds]
old_joint_traj_rs = old_joint_traj_rs[ds_inds]
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
new_joint_traj = mu.interp2d(np.arange(len(old_joint_traj)), np.arange(0, len(new_joint_traj) * args.trajopt_ds, args.trajopt_ds), new_joint_traj)
else:
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
if args.execution: Globals.pr2.update_rave()
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
redprint("Joint trajectory has length: " + str(len(bodypart2traj[part_name])))
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
#for lr in 'r':
# set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
if args.pid:
# Add trajectory to arrive at to initial state
redprint("Press enter to use current position")
raw_input()
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.r_arm_joint_names)
diff = np.array(arm_position - bodypart2traj['rarm'][0,:])
maximum = max(abs(diff))
speed = (300.0/360.0*2*(np.pi))
time_needed = maximum / speed
initial_pos_traj = mu.interp2d(np.arange(0, time_needed, 0.01), np.array([0,time_needed]), np.array([arm_position, bodypart2traj['rarm'][0,:]]))
# length of the intial trajectory, use this length for padding PD gains
initial_traj_length = initial_pos_traj.shape[0]
initial_force_traj = np.array([np.zeros(6) for i in range(initial_traj_length)])
temptraj = bodypart2traj['rarm']
bodypart2traj['rarm'] = np.concatenate((initial_pos_traj, temptraj), axis=0)
complete_force_traj = np.concatenate((initial_force_traj, new_eefs), axis=0)
# SECOND RESAMPLING HAPPENS HERE: JOINT VELOCITIES
if args.no_ds:
traj = bodypart2traj['rarm']
stamps = asarray(seg_info['stamps'])
times = np.array([stamps[i_end] - stamps[i_start]])
F = complete_force_traj
else:
times, times_up, traj = pr2_trajectories.return_timed_traj(Globals.pr2, bodypart2traj) # use times and times_up to interpolate the second time
F = mu.interp2d(times_up, times, complete_force_traj)
#Globals.robot.SetDOFValues(asarray(fake_seg["joint_states"]["position"][0]))
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, traj.shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj_rs[:,:3,3], 2, (0,0,1,1)))
# Setting robot arm to joint trajectory
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Idle()
# Controller code in joint space
traj_length = traj.shape[0]
pgains = np.asarray([2400.0, 1200.0, 1000.0, 700.0, 300.0, 300.0, 300.0])
dgains = np.asarray([18.0, 10.0, 6.0, 4.0, 6.0, 4.0, 4.0])
m = np.array([3.33, 1.16, 0.1, 0.25, 0.133, 0.0727, 0.0727]) # masses in joint space (feed forward)
vel_factor = 1e-3
#new costs
covariances, means = analyze_data.run_analyze_data(args)
CostsNew = []
counterCovs = 0
endTraj = False
covThiss = []
for covMat in covariances:
covThis = np.zeros((18,18))
covThis[0:6,0:6] = covMat[0:6,0:6]
covThis[12:18,12:18] = covMat[6:12, 6:12]
covThis[0:6, 12:18] = covMat[0:6, 6:12]
covThis[12:18, 0:6] = covMat[6:12, 0:6]
covThis[6:12, 6:12] = np.eye(6)*vel_factor
covThis = np.diag(np.diag(covThis))
covThiss.append(covThis)
#if len(covThiss) <= 69 and len(covThiss) >= 47:
# covThis[12:18,12:18] = np.diag([0.13, 0.06, 0.07, 0.005, 0.01, 0.004])
for j in range(args.eval.downsample_traj):
invCov = np.linalg.inv(covThis)
CostsNew.append(invCov)
counterCovs = counterCovs + 1
if counterCovs >= traj_length:
endTraj = True
break
if endTraj:
break
allCs = []
x = np.ones(6) * 1
v = np.ones(6) * 1e-3
a = np.ones(6) * 1e-6
Ct = np.diag(np.hstack((x, v, a))) # in end effector space
Ms = []
num_steps = i_end - i_start + 1
stamps = asarray(seg_info['stamps'][i_start:i_end+1])
arm = Globals.robot.GetManipulator("rightarm")
jacobians = []
for i in range(traj.shape[0]):
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
jacobians.append(np.vstack((arm.CalculateJacobian(), arm.CalculateAngularVelocityJacobian())))
jacobians = asarray(jacobians)
#jacobiansx = asarray(seg_info["jacobians"][i_start:i_end+1])
for t in range(num_steps):
M = np.zeros((18, 21))
J = jacobians[t]
M[0:6,0:7] = J
M[6:12,7:14] = J
mdiag = np.diag(m) # Convert joint feedforward values into diagonal array
mdiag_inv = np.linalg.inv(mdiag)
M[12:18,14:21] = np.linalg.inv((J.dot(mdiag_inv).dot(np.transpose(J)))).dot(J).dot(mdiag_inv)
Ms.append(M)
for t in range(num_steps):
topad = np.zeros((21,21))
topad[0:7,0:7] = np.diag(pgains) * 0.1
topad[7:14,7:14] = np.diag(dgains) * 0.1
topad[14:21,14:21] = np.eye(7) * 0.1
#allCs.append(np.transpose(Ms[t]).dot(Ct).dot(Ms[t]) + topad)
allCs.append(np.transpose(Ms[t]).dot(CostsNew[t]).dot(Ms[t]) + topad)
Kps = []
Kvs = []
Ks = []
Qt = None
Vs = []
for t in range(num_steps-1, -1, -1):
if Qt is None:
Qt = allCs[t]
else:
Ft = np.zeros((14, 21))
for j in range(14):
Ft[j][j] = 1.0
deltat = abs(stamps[t+1] - stamps[t])
#print(deltat)
for j in range(7):
Ft[j][j+7] = deltat
for j in range(7):
Ft[j+7][j+14] = deltat/m[j]
for j in range(7):
Ft[j][j+14] = ((deltat)**2)/m[j]
Qt = allCs[t] + (np.transpose(Ft).dot(Vs[num_steps-2-t]).dot(Ft))
Qxx = Qt[0:14, 0:14]
Quu = Qt[14:21, 14:21]
Qxu = Qt[0:14, 14:21]
Qux = Qt[14:21, 0:14]
Quuinv = np.linalg.inv(Quu)
Vt = Qxx - Qxu.dot(Quuinv).dot(Qux)
Vt = 0.5*(Vt + np.transpose(Vt))
Kt = -1*(Quuinv.dot(Qux))
Ks.append(Kt)
Kps.append(Kt[:, 0:7])
Kvs.append(Kt[:, 7:14])
Vs.append(Vt)
Ks = Ks[::-1]
Kps = Kps[::-1]
Kvs = Kvs[::-1]
JKpJ = np.asarray(Kps)
JKvJ = np.asarray(Kvs)
total_length = num_steps + initial_traj_length
# Pad initial traj with PD gains
pgainsdiag = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
dgainsdiag = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
addkp = np.asarray([pgainsdiag for i in range(initial_traj_length)])
addkv = np.asarray([dgainsdiag for i in range(initial_traj_length)])
JKpJ = np.concatenate((addkp, JKpJ), axis=0)
JKvJ = np.concatenate((addkv, JKvJ), axis=0)
Kps = []
Kvs = []
for i in range(num_steps + initial_traj_length):
Kps.append(np.zeros((6,6)))
Kvs.append(np.zeros((6,6)))
Kps = np.asarray(Kps)
Kvs = np.asarray(Kvs)
# Gains as JKpJ and JKvJ for testing
if args.pdgains:
JKpJ = np.asarray([pgainsdiag for i in range(total_length)])
JKvJ = np.asarray([dgainsdiag for i in range(total_length)])
qs = traj
IPython.embed()
Kps = np.resize(Kps, (1, 36 * total_length))[0]
Kvs = np.resize(Kvs, (1, 36 * total_length))[0]
JKvJ = np.resize(JKvJ, (1, 49*total_length))[0]
JKpJ = np.resize(JKpJ, (1, 49*total_length))[0]
# For use with new controller
qs = np.resize(qs, (1, qs.shape[0]*7))[0] #resize the data to 1x7n (n being the number of steps)
F = np.resize(F, (1, F.shape[0]*6))[0] #resize the data to 1x6n
# [traj, Kp, Kv, F, use_force, seconds]
data = np.zeros((1, len(qs) + len(JKpJ) + len(JKvJ) + len(F) + len(Kps) + len(Kvs) + 2))
data[0][0:len(qs)] = qs
data[0][len(qs):len(qs)+len(JKpJ)] = JKpJ
data[0][len(qs)+len(JKpJ):len(qs)+len(JKpJ)+len(JKvJ)] = JKvJ
data[0][len(qs)+len(JKpJ)+len(JKvJ):len(qs)+len(JKpJ)+len(JKvJ)+len(F)] = F
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)] = Kps
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)+len(Kvs)] = Kvs
data[0][-2] = args.use_force
data[0][-1] = stamps[i_end] - stamps[i_start] + (initial_traj_length * 0.01)
# For use with old controller
"""
qs = np.array(np.matrix(traj).T) # 7 x n
qs = np.resize(qs, (1, qs.shape[1]*7))[0] #resize the data to 1x7n (n being the number of steps)
F = np.array(np.matrix(F).T) # 6 x n
F = F[0:3,:]
F = np.resize(F, (1, F.shape[1]*3))[0] #resize the data to 1x3n
data = np.zeros((1, len(qs) + len(F) + 2))
data[0][0:len(qs)] = qs
data[0][len(qs):len(qs)+len(F)] = F
data[0][-1] = args.use_force
data[0][-2] = stamps[i_end] - stamps[i_start]
"""
msg = Float64MultiArray()
msg.data = data[0].tolist()
pub = rospy.Publisher("/controller_data", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
else:
#if not success: break
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
def loop_body(demofile, choose_segment, knot, animate, task_params, curr_step=None):
"""Do the body of the main task execution loop (ie. do a segment).
Arguments:
curr_step is 0 indexed
choose_segment is a function that returns the key in the demofile to the segment
knot is the knot the rope is checked against
new_xyz is the new pointcloud
task_params is used for the only_original_segments argument
return None or {'found_knot': found_knot, 'segment': segment, 'link2eetraj': link2eetraj, 'new_xyz': new_xyz}
"""
#TODO -- Return the new trajectory and state info to be used for bootstrapping (knot_success, new_xyz, link2eetraj,
#TODO segment)
#TODO -- End condition
#TODO -- max_segments logic
redprint("Acquire point cloud")
move_sim_arms_to_side()
#TODO -- Possibly refactor this section to be before the loop
new_xyz = Globals.sim.observe_cloud()
new_xyz_upsampled = Globals.sim.observe_cloud(upsample=120)
print "loop_body only_original_segments", task_params.only_original_segments
segment = choose_segment(demofile, new_xyz, task_params.only_original_segments)
if segment is None:
return None
seg_info = demofile[segment]
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1)))
#TODO: test that old_xyz is now bigger if from a derived segment
#old_xyz = clouds.downsample(old_xyz, DS_SIZE)
if not "derived" in seg_info.keys():
old_xyz = downsample(old_xyz, DS_SIZE)
print "derived, so downsamping"
#new_xyz = clouds.downsample(new_xyz, DS_SIZE)
#new_xyz = downsample_if_big(new_xyz, DS_SIZE)
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0)))
print "new_xyz cloud size", new_xyz.shape
print "old_xyz cloud size", old_xyz.shape
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
#TODO: get rid of g
f, g = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb=tpsrpm_plot_cb,
plotting=5 if animate else 0, rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=old_xyz, new_xyz=new_xyz)
f = registration.unscale_tps(f, src_params, targ_params)
g = registration.unscale_tps(g, src_params, targ_params)
#Globals.exec_log(curr_step, "gen_traj.f", f)
#handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
# old_xyz.max(axis=0) + np.r_[0, 0, .1], xres=.1, yres=.1, zres=.04))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
old_xyz.max(axis=0) + np.r_[0, 0, .02], xres=.01, yres=.01, zres=.04))
#handles.extend(plotting_openrave.draw_grid(Globals.env, g.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
# old_xyz.max(axis=0) + np.r_[0, 0, .02], xres=.01, yres=.01, zres=.04))
link2eetraj = {}
#link2eetraj is a hash of gripper fram to new trajectory
#Transform the gripper trajectory here
for lr in 'lr':
link_name = "%s_gripper_tool_frame" % lr
#old_ee_traj is the old gripper trajectory
old_ee_traj = asarray(seg_info[link_name]["hmat"])
#new_ee_traj is the transformed gripper trajectory
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#Draw the old and new gripper trajectories as lines
handles.append(Globals.env.drawlinestrip(old_ee_traj[:, :3, 3], 2, (1, 0, 0, 1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
#Globals.exec_log(curr_step, "gen_traj.link2eetraj", link2eetraj)
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
#print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
#TODO: modify for no body
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
################################
redprint("Generating joint trajectory for segment %s, part %i" % (segment, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
#old_joint_traj is the old trajectory inside the minisegment
old_joint_traj = asarray(seg_info[manip_name][i_start: i_end + 1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr, old_joint_traj) in lr2oldtraj.items():
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame" % lr
new_ee_traj = link2eetraj[ee_link_name][i_start: i_end + 1]
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
#Call the planner (eg. trajopt)
with dhm_u.suppress_stdout():
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,
old_joint_traj_rs)
part_name = {"l": "larm", "r": "rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
### Execute the gripper ###
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'" % (
segment, i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
gripper_open = binarize_gripper(seg_info["%s_gripper_joint" % lr][i_start])
prev_gripper_open = binarize_gripper(
seg_info["%s_gripper_joint" % lr][i_start - 1]) if i_start != 0 else False
if not set_gripper_sim(lr, gripper_open, prev_gripper_open, animate):
redprint("Grab %s failed" % lr)
success = False
if not success:
break
# Execute the robot trajectory
if len(bodypart2traj) > 0:
success &= exec_traj_sim(bodypart2traj, animate)
#Globals.exec_log(curr_step, "execute_traj.miniseg_%d.sim_rope_nodes_after_traj" % i_miniseg, Globals.sim.rope.GetNodes())
if not success:
break
Globals.sim.settle(animate=animate)
if Globals.exec_log:
Globals.exec_log(curr_step, "execute_traj.sim_rope_nodes_after_full_traj", Globals.sim.rope.GetNodes())
from rapprentice import knot_identification
knot_name = knot_identification.identify_knot(Globals.sim.rope.GetControlPoints())
found_knot = False
if knot_name is not None:
if knot_name == knot or knot == "any":
redprint("Identified knot: %s. Success!" % knot_name)
#Globals.exec_log(curr_step, "result", True, description="identified knot %s" % knot_name)
found_knot = True
else:
redprint("Identified knot: %s, but expected %s. Continuing." % (knot_name, knot))
else:
redprint("Not a knot. Continuing.")
redprint("Segment %s result: %s" % (segment, success))
return {'found_knot': found_knot, 'segment': segment, 'link2eetraj': link2eetraj, 'new_xyz': new_xyz_upsampled}
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
new_xyz = np.squeeze(demofile[args.fake_data_segment]["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
else:
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
Globals.pr2.join_all()
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
################################
redprint("Finding closest demonstration")
if args.fake_data_segment:
seg_name = args.fake_data_segment
else:
seg_name = find_closest(demofile, new_xyz)
seg_info = demofile[seg_name]
# redprint("using demo %s, description: %s"%(seg_name, seg_info["description"]))
################################
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz,5, (1,0,0,1)))
handles.append(Globals.env.plot3(new_xyz,5, (0,0,1,1)))
f = registration.tps_rpm(old_xyz, new_xyz, plot_cb = tpsrpm_plot_cb,plotting=1)
#f = registration.ThinPlateSpline() XXX XXX
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0), old_xyz.max(axis=0), xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
# TODO plot
# plot_warping_and_trajectories(f, old_xyz, new_xyz, old_ee_traj, new_ee_traj)
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
bodypart2traj = {}
arms_used = ""
for lr in 'lr':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
if arm_moved(old_joint_traj):
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name]
if args.execution: Globals.pr2.update_rave()
new_joint_traj = plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj[i_start:i_end+1],
old_joint_traj)
# (robot, manip_name, ee_link, new_hmats, old_traj):
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
arms_used += lr
################################
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, arms_used))
for lr in 'lr':
set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
#trajoptpy.GetViewer(Globals.env).Idle()
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
# TODO measure failure condtions
if not success:
break
redprint("Segment %s result: %s"%(seg_name, success))
if args.fake_data_segment: break
def get_warped_trajectory(seg_info, new_xyz, demofile, warp_root=False, plot=False, no_cmat=False):
"""
@seg_info : segment information from the h5 file for the segment with least tps fit cost.
@new_xyz : point cloud of the rope in the test situation.
@warp_root : warp the root trajectory if True else warp the chosen segment's trajectory.
@plot : plot the warping as we run TPS-RPM
@no_cmat : if True, does not use the correspondence matrix to the root to initialize TPS-RPM
@returns : the warped trajectory for l/r grippers and the mini-segment information.
"""
handles = []
seg_xyz = seg_info["cloud_xyz"][:]
scaled_seg_xyz, seg_params = registration.unit_boxify(seg_xyz)
scaled_new_xyz, new_params = registration.unit_boxify(new_xyz)
if plot:
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1)))
handles.append(Globals.env.plot3(seg_xyz, 5, (1, 0, 0)))
root_seg_name = seg_info['root_seg']
root_segment = demofile[root_seg_name.value]
root_xyz = root_segment['cloud_xyz'][:]
seg_root_cmat = seg_info['cmat'][:]
if warp_root:
## transfer the trajectory from the root demonstration this demonstration is derived from
scaled_root_xyz, root_params = registration.unit_boxify(root_xyz)
if no_cmat:
## find the correspondences from the root again
## only use the bootstrapping for action selection
print "not using cmat for correspondences"
f_root2new, _, corr_new2root = registration.tps_rpm_bij(scaled_root_xyz, scaled_new_xyz,
plotting=5 if plot else 0, plot_cb=tpsrpm_plot_cb,
rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=root_xyz, new_xyz=new_xyz,
return_corr=True)
else:
## use the information from the bootstrapping example to initialize correspondences
## uses bootstrapping to select demonstration to transfer and uses information from that transfer to improve warp
f_root2new, _, corr_new2root = registration.tps_rpm_bootstrap(scaled_root_xyz, scaled_seg_xyz, scaled_new_xyz, seg_root_cmat,
plotting=5 if plot else 0, plot_cb=tpsrpm_plot_cb,
rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=root_xyz, new_xyz=new_xyz)
f_warping = registration.unscale_tps(f_root2new, root_params, new_params)
old_ee_traj = root_segment['hmats']
rgrip_joints = root_segment['r_gripper_joint'][:]
lgrip_joints = root_segment['l_gripper_joint'][:]
cmat = corr_new2root
else:
## warp to the chosen segment, use an iterated TPS to transfer the root trajectory most use of bootstrapping
f_seg2new, _, corr_new2seg = registration.tps_rpm_bij(scaled_seg_xyz, scaled_new_xyz, plot_cb=tpsrpm_plot_cb,
plotting=5 if plot else 0, rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=seg_xyz, new_xyz=new_xyz,
return_corr=True)
f_warping = registration.unscale_tps(f_seg2new, seg_params, new_params)
old_ee_traj = seg_info['hmats']
rgrip_joints = root_segment['r_gripper_joint'][:]
lgrip_joints = root_segment['l_gripper_joint'][:]
cmat = seg_root_cmat.dot(corr_new2seg)
if compare_bootstrap_correspondences:
## used to compare b/t warping root and warping derived demonstrations
scaled_root_xyz, root_params = registration.unit_boxify(root_xyz)
f_root2new, _, corr_new2root = registration.tps_rpm_bootstrap(scaled_root_xyz, scaled_seg_xyz, scaled_new_xyz, seg_root_cmat,
plotting=5 if plot else 0, plot_cb=tpsrpm_plot_cb,
rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=root_xyz, new_xyz=new_xyz)
root_warping = registration.unscale_tps(f_root2new, root_params, new_params)
root_ee_traj = root_segment['hmats']
diff = 0
for lr in 'lr':
no_root_ee_traj = f_warping.transform_hmats(old_ee_traj[lr][:])
warped_root_ee_traj = root_warping.transform_hmats(root_ee_traj[lr][:])
diff += np.linalg.norm(no_root_ee_traj - warped_root_ee_traj)
handles.append(Globals.env.drawlinestrip(old_ee_traj[lr][:, :3, 3], 2, (1, 0, 0, 1)))
handles.append(Globals.env.drawlinestrip(no_root_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
handles.append(Globals.env.drawlinestrip(warped_root_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
if plot:
print 'traj norm differences:\t', diff
Globals.viewer.Idle()
if plot:
handles.extend(plotting_openrave.draw_grid(Globals.env, f_warping.transform_points, new_xyz.min(axis=0) - np.r_[0, 0, .1],
new_xyz.max(axis=0) + np.r_[0, 0, .02], xres=.01, yres=.01, zres=.04))
warped_ee_traj = {}
#Transform the gripper trajectory here
for lr in 'lr':
new_ee_traj = f_warping.transform_hmats(old_ee_traj[lr][:])
warped_ee_traj[lr] = new_ee_traj
if plot:
handles.append(Globals.env.drawlinestrip(old_ee_traj[lr][:, :3, 3], 2, (1, 0, 0, 1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(rgrip_joints, lgrip_joints)
return (cmat, warped_ee_traj, miniseg_starts, miniseg_ends, {'r':rgrip_joints, 'l':lgrip_joints})
def main():
import IPython
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
else:
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
print "got new xyz"
redprint(new_xyz)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
### Old end effector forces at r_gripper_tool_frame (eliminating the torques for now)
old_eefs = seg_info['end_effector_forces'][:,0:3,:]
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
old_xyz_transformed = f.transform_points(old_xyz)
#handles.append(Globals.env.plot3(old_xyz_transformed,5, np.array([(0,0,1,1) for i in old_xyz_transformed])))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'r':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#print old_ee_traj
old_ee_pos = old_ee_traj[:,0:3,3]
#print old_ee_pos
# End effector forces as oppossed to end effector trajectories
dfdxs = f.compute_jacobian(old_ee_pos)
new_eefs = []
for i in xrange(len(old_eefs)):
dfdx = np.matrix(dfdxs[i])
old_eef = np.matrix(old_eefs[i])
new_eefs.append(dfdx * old_eef)
old_eefs = asarray(old_eefs)[:,:,0]
new_eefs = asarray(new_eefs)[:,:,0]
force_data = {}
force_data['old_eefs'] = old_eefs
force_data['new_eefs'] = new_eefs
force_file = open("trial.pickle", 'wa')
pickle.dump(force_data, force_file)
force_file.close()
new_ee_traj_x = new_ee_traj
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'r':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
#if arm_moved(old_joint_traj): NOT SURE WHY BUT THIS IS RETURNING FALSE
lr2oldtraj[lr] = old_joint_traj
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, lr2oldtraj['r'].shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
# Setting robot arm to joint trajectory
r_vals = lr2oldtraj['r'][i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(old_eefs[i])/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
# FIRST RESAMPLING HAPPENS HERE: JOINT_LENGTH
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP) # Timesteps only, can use to inter eefs for first time
####
new_eefs_segment = asarray(new_eefs[i_start:i_end+1,:]) # Extract miniseg, and re-sample
if args.no_ds:
new_eefs_segment_rs = new_eefs_segment
else:
new_eefs_segment_rs = mu.interp2d(timesteps_rs, np.arange(len(new_eefs_segment)), new_eefs_segment)
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
if args.no_ds:
old_joint_traj_rs = old_joint_traj
new_ee_traj_rs = new_ee_traj
ds_inds = np.arange(0, len(new_ee_traj_rs), args.trajopt_ds)
new_ee_traj_rs = new_ee_traj_rs[ds_inds]
old_joint_traj_rs = old_joint_traj_rs[ds_inds]
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
new_joint_traj = mu.interp2d(np.arange(len(old_joint_traj)), np.arange(0, len(new_joint_traj) * args.trajopt_ds, args.trajopt_ds), new_joint_traj)
else:
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
if args.execution: Globals.pr2.update_rave()
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
redprint("Joint trajectory has length: " + str(len(bodypart2traj[part_name])))
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
if args.pid:
if not args.fake_data_segment: # If running on PR2, add initial state as waypoint and rough interpolate
# Add initial position
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.arm_joint_names)
traj_length = bodypart2traj['rarm'].shape[0]
complete_joint_traj = np.zeros((traj_length+1, 7)) # To include initial state as a way point
complete_joint_traj[1:,:] = bodypart2traj['rarm']
complete_joint_traj[0,:] = arm_position
bodypart2traj['rarm'] = complete_joint_traj
# Add initial eff
J = np.matrix(np.resize(np.array(robot_states.call_return_jacobian()), (6, 7))) # Jacobian
eff = robot_states.compute_end_effector_force(J, arm_effort).T
eff = np.array(eff)[0]
init_force = eff[:3]
complete_force_traj = np.zeros((traj_length+1, 3))
complete_force_traj[1:,:] = new_eefs_segment_rs
complete_force_traj[0,:] = init_force
else:
complete_force_traj = new_eefs_segment_rs
# SECOND RESAMPLING HAPPENS HERE: JOINT VELOCITIES
if args.no_ds:
traj = bodypart2traj['rarm']
stamps = asarray(seg_info['stamps'])
times = np.array([stamps[i_end] - stamps[i_start]])
force = complete_force_traj
else:
times, times_up, traj = pr2_trajectories.return_timed_traj(Globals.pr2, bodypart2traj) # use times and times_up to interpolate the second time
force = mu.interp2d(times_up, times, complete_force_traj)
#Globals.robot.SetDOFValues(asarray(fake_seg["joint_states"]["position"][0]))
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, traj.shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj_rs[:,:3,3], 2, (0,0,1,1)))
# Setting robot arm to joint trajectory
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(force[i])/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
traj = np.array(np.matrix(traj).T) # 7 x n
redprint(traj)
traj = np.resize(traj, (1, traj.shape[1]*7)) #resize the data to 1x7n (n being the number of steps)
force = np.array(np.matrix(force).T) # 3 x n
force = np.resize(force, (1, force.shape[1]*3)) #resize the data to 1x3n
#[traj, force, secs]
traj_force_secs = np.zeros((1, traj.shape[1] + force.shape[1] + 2))
traj_force_secs[0,0:traj.shape[1]] = traj
traj_force_secs[0,traj.shape[1]:traj.shape[1]+force.shape[1]] = force
traj_force_secs[0,traj.shape[1]+force.shape[1]] = times[len(times)-1]
traj_force_secs[0,traj.shape[1]+force.shape[1]+1] = args.use_force
IPython.embed()
msg = Float64MultiArray()
msg.data = traj_force_secs[0].tolist()
pub = rospy.Publisher("/joint_positions_forces_secs", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
else:
#if not success: break
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
print "%.3f elapsed"%(time() - tstart)
cost = reg.tps_reg_cost(fest_scaled) + reg.tps_reg_cost(gest_scaled)
print "cost: %.3f"%cost
import matplotlib.pyplot as plt
plt.clf()
# plt.plot(xys[:,1], xys[:,0],'r.')
# plt.plot(fxys[:,1], fxys[:,0],'b.')
# plt.plot(fxys_est[:,1], fxys_est[:,0],'g.')
scaled_fcloud = fest_scaled.transform_points(scaled_cloud0)
plt.plot(scaled_cloud0[:,1], scaled_cloud0[:,0],'r.')
plt.plot(scaled_cloud1[:,1], scaled_cloud1[:,0],'b.')
plt.plot(scaled_fcloud[:,1], scaled_fcloud[:,0],'g.')
if dim == 2:
from rapprentice.plotting_plt import plot_warped_grid_2d
plot_warped_grid_2d(fest_scaled.transform_points, [-.5,-.5], [.5,.5])
elif dim == 3:
env= openravepy.RaveGetEnvironment(1)
if env is None: env = openravepy.Environment()
from rapprentice.plotting_openrave import draw_grid
draw_grid(fest_scaled, [-.5,-.5,-.5], [.5,.5,.5])
plt.draw()
raw_input()
# plt.plot(fxys[:,1], fxys[:,0],'b.')
# plt.plot(fxys_est[:,1], fxys_est[:,0],'g.')
plt.plot(scaled_cloud0[:,1], scaled_cloud0[:,0],'r.')
plt.plot(scaled_cloud1[:,1], scaled_cloud1[:,0],'b.')
plt.plot(scaled_fcloud[:,1], scaled_fcloud[:,0],'g.')
from rapprentice.plotting_plt import plot_warped_grid_2d
plot_warped_grid_2d(fest_scaled.transform_points, [-.5,-.5], [.5,.5])
plt.draw()
raw_input()
elif dim == 3:
env= openravepy.RaveGetEnvironment(1)
if env is None: env = openravepy.Environment()
import trajoptpy
viewer = trajoptpy.GetViewer(env)
if env is None: env = openravepy.Environment()
from rapprentice.plotting_openrave import draw_grid
handles = []
handles.append(env.plot3(scaled_cloud0,5,(1,0,0,1)))
handles.append(env.plot3(scaled_cloud1,5,(0,0,1,1)))
handles.append(env.plot3(scaled_fcloud,5,(0,1,0,1)))
handles.extend(draw_grid(env, fest_scaled.transform_points, [-.5,-.5,-.1], [.5,.5,.1]))
viewer.Idle()
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.execution:
rospy.init_node("exec_task", disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
subprocess.call("killall XnSensorServer", shell=True)
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
new_xyz = np.squeeze(demofile[args.fake_data_segment]["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(
args.fake_data_transform[3:6])
hmat[:3, 3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3, :3].T) + hmat[:3, 3][None, :]
else:
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
Globals.pr2.join_all()
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
################################
redprint("Finding closest demonstration")
if args.fake_data_segment:
seg_name = args.fake_data_segment
elif args.choice == "costs":
f, seg_name = choose_segment(demofile, new_xyz)
else:
seg_name = choose_segment(demofile, new_xyz)
seg_info = demofile[seg_name]
# redprint("using demo %s, description: %s"%(seg_name, seg_info["description"]))
################################
redprint("Generating end-effector trajectory")
if not args.choice == "costs":
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0, 1)))
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1, 1)))
f = registration.tps_rpm(old_xyz,
new_xyz,
plot_cb=tpsrpm_plot_cb,
plotting=1)
handles.extend(
plotting_openrave.draw_grid(Globals.env,
f.transform_points,
old_xyz.min(axis=0),
old_xyz.max(axis=0),
xres=.1,
yres=.1,
zres=.04))
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame" % lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
if not args.choice == "costs":
handles.append(
Globals.env.drawlinestrip(old_ee_traj[:, :3, 3], 2,
(1, 0, 0, 1)))
handles.append(
Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2,
(0, 1, 0, 1)))
# TODO plot
# plot_warping_and_trajectories(f, old_xyz, new_xyz, old_ee_traj, new_ee_traj)
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:",
"red"), miniseg_starts, miniseg_ends
for (i_miniseg,
(i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution == "real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i" %
(seg_name, i_miniseg))
bodypart2traj = {}
arms_used = ""
for lr in 'lr':
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end +
1])
if arm_moved(old_joint_traj):
ee_link_name = "%s_gripper_tool_frame" % lr
new_ee_traj = link2eetraj[ee_link_name]
if args.execution: Globals.pr2.update_rave()
new_joint_traj = plan_follow_traj(
Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name),
new_ee_traj[i_start:i_end + 1], old_joint_traj)
# (robot, manip_name, ee_link, new_hmats, old_traj):
part_name = {"l": "larm", "r": "rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
arms_used += lr
################################
redprint(
"Executing joint trajectory for segment %s, part %i using arms '%s'"
% (seg_name, i_miniseg, arms_used))
for lr in 'lr':
set_gripper_maybesim(
lr,
binarize_gripper(seg_info["%s_gripper_joint" %
lr][i_start]))
#trajoptpy.GetViewer(Globals.env).Idle()
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
# TODO measure failure condtions
if not success:
break
redprint("Segment %s result: %s" % (seg_name, success))
if args.fake_data_segment: break
def get_warped_trajectory(seg_info, new_xyz, demofile, warp_root=True, plot=False, no_cmat=False):
"""
@seg_info : segment information from the h5 file for the segment with least tps fit cost.
@new_xyz : point cloud of the rope in the test situation.
@warp_root : warp the root trajectory if True else warp the chosen segment's trajectory.
@returns : the warped trajectory for l/r grippers and the mini-segment information.
"""
print "****WARP ROOT*** : ", warp_root
print "****NO CMAT*** : ", no_cmat
handles = []
seg_xyz = seg_info["cloud_xyz"][:]
scaled_seg_xyz, seg_params = registration.unit_boxify(seg_xyz)
scaled_new_xyz, new_params = registration.unit_boxify(new_xyz)
if plot:
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1)))
handles.append(Globals.env.plot3(seg_xyz, 5, (1, 0, 0)))
root_seg_name = seg_info['root_seg']
root_segment = demofile[root_seg_name.value]
root_xyz = root_segment['cloud_xyz'][:]
seg_root_cmat = seg_info['cmat'][:]
if warp_root:
scaled_root_xyz, root_params = registration.unit_boxify(root_xyz)
if no_cmat:
print "not using cmat for correspondences"
f_root2new, _, corr_new2root = registration.tps_rpm_bij(scaled_root_xyz, scaled_new_xyz,
plotting=5 if plot else 0, plot_cb=tpsrpm_plot_cb,
rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=root_xyz, new_xyz=new_xyz,
return_corr=True)
else:
## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
## TODO : MAKE SURE THAT THE SCALING IS BEING DONE CORRECTLY HERE:
## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
f_root2new, _, corr_new2root = registration.tps_rpm_bootstrap(scaled_root_xyz, scaled_seg_xyz, scaled_new_xyz, seg_root_cmat,
plotting=5 if plot else 0, plot_cb=tpsrpm_plot_cb,
rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=root_xyz, new_xyz=new_xyz)
f_warping = registration.unscale_tps(f_root2new, root_params, new_params)
old_ee_traj = root_segment['hmats']
rgrip_joints = root_segment['r_gripper_joint'][:]
lgrip_joints = root_segment['l_gripper_joint'][:]
cmat = corr_new2root
else: ## warp to the chosen segment:
f_seg2new, _, corr_new2seg = registration.tps_rpm_bij(scaled_seg_xyz, scaled_new_xyz, plot_cb=tpsrpm_plot_cb,
plotting=5 if plot else 0, rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=seg_xyz, new_xyz=new_xyz,
return_corr=True)
f_warping = registration.unscale_tps(f_seg2new, seg_params, new_params)
old_ee_traj = seg_info['hmats']
rgrip_joints = root_segment['r_gripper_joint'][:]
lgrip_joints = root_segment['l_gripper_joint'][:]
cmat = seg_root_cmat.dot(corr_new2seg)
if compare_bootstrap_correspondences:
scaled_root_xyz, root_params = registration.unit_boxify(root_xyz)
f_root2new, _, corr_new2root = registration.tps_rpm_bootstrap(scaled_root_xyz, scaled_seg_xyz, scaled_new_xyz, seg_root_cmat,
plotting=5 if plot else 0, plot_cb=tpsrpm_plot_cb,
rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=root_xyz, new_xyz=new_xyz)
root_warping = registration.unscale_tps(f_root2new, root_params, new_params)
root_ee_traj = root_segment['hmats']
diff = 0
for lr in 'lr':
no_root_ee_traj = f_warping.transform_hmats(old_ee_traj[lr][:])
warped_root_ee_traj = root_warping.transform_hmats(root_ee_traj[lr][:])
diff += np.linalg.norm(no_root_ee_traj - warped_root_ee_traj)
handles.append(Globals.env.drawlinestrip(old_ee_traj[lr][:, :3, 3], 2, (1, 0, 0, 1)))
handles.append(Globals.env.drawlinestrip(no_root_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
handles.append(Globals.env.drawlinestrip(warped_root_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
if plot:
print 'traj norm differences:\t', diff
Globals.viewer.Idle()
if plot:
handles.extend(plotting_openrave.draw_grid(Globals.env, f_warping.transform_points, new_xyz.min(axis=0) - np.r_[0, 0, .1],
new_xyz.max(axis=0) + np.r_[0, 0, .02], xres=.01, yres=.01, zres=.04))
warped_ee_traj = {}
#Transform the gripper trajectory here
for lr in 'lr':
new_ee_traj = f_warping.transform_hmats(old_ee_traj[lr][:])
warped_ee_traj[lr] = new_ee_traj
if plot:
handles.append(Globals.env.drawlinestrip(old_ee_traj[lr][:, :3, 3], 2, (1, 0, 0, 1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(rgrip_joints, lgrip_joints)
return (cmat, warped_ee_traj, miniseg_starts, miniseg_ends, {'r':rgrip_joints, 'l':lgrip_joints})
print "%.3f elapsed" % (time() - tstart)
cost = reg.tps_reg_cost(fest_scaled) + reg.tps_reg_cost(
gest_scaled)
print "cost: %.3f" % cost
import matplotlib.pyplot as plt
plt.clf()
# plt.plot(xys[:,1], xys[:,0],'r.')
# plt.plot(fxys[:,1], fxys[:,0],'b.')
# plt.plot(fxys_est[:,1], fxys_est[:,0],'g.')
scaled_fcloud = fest_scaled.transform_points(scaled_cloud0)
plt.plot(scaled_cloud0[:, 1], scaled_cloud0[:, 0], 'r.')
plt.plot(scaled_cloud1[:, 1], scaled_cloud1[:, 0], 'b.')
plt.plot(scaled_fcloud[:, 1], scaled_fcloud[:, 0], 'g.')
if dim == 2:
from rapprentice.plotting_plt import plot_warped_grid_2d
plot_warped_grid_2d(fest_scaled.transform_points, [-.5, -.5],
[.5, .5])
elif dim == 3:
env = openravepy.RaveGetEnvironment(1)
if env is None: env = openravepy.Environment()
from rapprentice.plotting_openrave import draw_grid
draw_grid(fest_scaled, [-.5, -.5, -.5], [.5, .5, .5])
plt.draw()
raw_input()
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"),
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task", disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(
args.fake_data_transform[3:6])
hmat[:3, 3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3, :3].T) + hmat[:3, 3][None, :]
r2r = ros2rave.RosToRave(Globals.robot,
asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot,
asarray(fake_seg["joint_states"]["position"][0]))
else:
Globals.pr2.head.set_pan_tilt(0, 1.2)
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
Globals.pr2.join_all()
time.sleep(.5)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR, "rgb%i.png" % LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR, "depth%i.png" % LOG_COUNT), depth)
np.save(osp.join(LOG_DIR, "xyz%i.npy" % LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s" % (seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR, "neighbor%i.txt" % LOG_COUNT),
"w") as fh:
fh.write(seg_name)
################################
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0)))
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1)))
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f, _ = registration.tps_rpm_bij(scaled_old_xyz,
scaled_new_xyz,
plot_cb=tpsrpm_plot_cb,
plotting=5 if args.animation else 0,
rot_reg=np.r_[1e-4, 1e-4, 1e-1],
n_iter=50,
reg_init=10,
reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
handles.extend(
plotting_openrave.draw_grid(Globals.env,
f.transform_points,
old_xyz.min(axis=0) - np.r_[0, 0, .1],
old_xyz.max(axis=0) + np.r_[0, 0, .1],
xres=.1,
yres=.1,
zres=.04))
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame" % lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
handles.append(
Globals.env.drawlinestrip(old_ee_traj[:, :3, 3], 2,
(1, 0, 0, 1)))
handles.append(
Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2,
(0, 1, 0, 1)))
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:",
"red"), miniseg_starts, miniseg_ends
for (i_miniseg,
(i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution == "real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i" %
(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end +
1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj,
JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr, old_joint_traj) in lr2oldtraj.items():
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs,
np.arange(len(old_joint_traj)),
old_joint_traj)
ee_link_name = "%s_gripper_tool_frame" % lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end + 1]
new_ee_traj_rs = resampling.interp_hmats(
timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
if args.execution: Globals.pr2.update_rave()
new_joint_traj = planning.plan_follow_traj(
Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,
old_joint_traj_rs)
part_name = {"l": "larm", "r": "rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint(
"Executing joint trajectory for segment %s, part %i using arms '%s'"
% (seg_name, i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
success &= set_gripper_maybesim(
lr,
binarize_gripper(seg_info["%s_gripper_joint" %
lr][i_start]))
# Doesn't actually check if grab occurred, unfortunately
if not success: break
if len(bodypart2traj) > 0:
success &= exec_traj_maybesim(bodypart2traj)
if not success: break
redprint("Segment %s result: %s" % (seg_name, success))
if args.fake_data_segment: break
def compute_trans_traj(sim_env, new_xyz, seg_info, ignore_infeasibility=True, animate=False, interactive=False):
redprint("Generating end-effector trajectory")
old_xyz = np.squeeze(seg_info["cloud_xyz"])
old_xyz = clouds.downsample(old_xyz, DS_SIZE)
l1 = len(old_xyz)
new_xyz = clouds.downsample(new_xyz, DS_SIZE)
l2 = len(new_xyz)
print l1, l2
link_names = ["%s_gripper_tool_frame"%lr for lr in ('lr')]
hmat_list = [(lr, seg_info[ln]['hmat']) for lr, ln in zip('lr', link_names)]
if GlobalVars.gripper_weighting:
interest_pts = get_closing_pts(seg_info)
else:
interest_pts = None
lr2eetraj, _, old_xyz_warped, f = warp_hmats_tfm(old_xyz, new_xyz, hmat_list, interest_pts)
handles = []
if animate:
handles.extend(plotting_openrave.draw_grid(sim_env.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
handles.append(sim_env.env.plot3(old_xyz,5, (1,0,0))) # red: demonstration point cloud
handles.append(sim_env.env.plot3(new_xyz,5, (0,0,1))) # blue: rope nodes
handles.append(sim_env.env.plot3(old_xyz_warped,5, (0,1,0))) # green: warped point cloud from demonstration
mapped_pts = []
for i in range(len(old_xyz)):
mapped_pts.append(old_xyz[i])
mapped_pts.append(old_xyz_warped[i])
handles.append(sim_env.env.drawlinelist(np.array(mapped_pts), 1, [0.1,0.1,1]))
for lr in 'lr':
handles.append(sim_env.env.drawlinestrip(lr2eetraj[lr][:,:3,3], 2, (0,1,0,1)))
for k, hmats in hmat_list:
hmats_tfm = np.asarray([GlobalVars.init_tfm.dot(h) for h in hmats])
handles.append(sim_env.env.drawlinestrip(hmats_tfm[:,:3,3], 2, (1,0,0,1)))
miniseg_starts, miniseg_ends, lr_open = sim_util.split_trajectory_by_gripper(seg_info, thresh=GRIPPER_ANGLE_THRESHOLD)
success = True
feasible = True
misgrasp = False
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
miniseg_trajs = []
prev_vals = {lr:None for lr in 'lr'}
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
################################
redprint("Generating joint trajectory for part %i"%(i_miniseg))
### adaptive resampling based on xyz in end_effector
end_trans_trajs = np.zeros([i_end+1-i_start, 6])
for lr in 'lr':
for i in xrange(i_start,i_end+1):
if lr == 'l':
end_trans_trajs[i-i_start, :3] = lr2eetraj[lr][i][:3,3]
else:
end_trans_trajs[i-i_start, 3:] = lr2eetraj[lr][i][:3,3]
if True:
adaptive_times, end_trans_trajs = resampling.adaptive_resample2(end_trans_trajs, 0.005)
else:
adaptive_times = range(len(end_trans_trajs))
miniseg_traj = {}
for lr in 'lr':
#ee_hmats = resampling.interp_hmats(np.arange(i_end+1-i_start), np.arange(i_end+1-i_start), lr2eetraj[lr][i_start:i_end+1])
ee_hmats = resampling.interp_hmats(adaptive_times, np.arange(i_end+1-i_start), lr2eetraj[lr][i_start:i_end+1])
# the interpolation above will then the velocity of the trajectory (since there are fewer waypoints). Resampling again to make sure
# the trajectory has the same number of waypoints as before.
ee_hmats = resampling.interp_hmats(np.arange(i_end+1-i_start), adaptive_times, ee_hmats)
# if arm_moved(ee_hmats, floating=True):
if True:
miniseg_traj[lr] = ee_hmats
safe_drop = {'l': True, 'r': True}
for lr in 'lr':
next_gripper_open = lr_open[lr][i_miniseg+1] if i_miniseg < len(miniseg_starts) - 1 else False
gripper_open = lr_open[lr][i_miniseg]
if next_gripper_open and not gripper_open:
tfm = miniseg_traj[lr][-1]
if tfm[2,3] > GlobalVars.table_height + 0.01:
safe_drop[lr] = False
#safe_drop = {'l': True, 'r': True}
if not (safe_drop['l'] and safe_drop['r']):
for lr in 'lr':
if not safe_drop[lr]:
tfm = miniseg_traj[lr][-1]
for i in range(1, 8):
safe_drop_tfm = tfm
safe_drop_tfm[2,3] = tfm[2,3] - i / 10. * (tfm[2,3] - GlobalVars.table_height - 0.01)
miniseg_traj[lr].append(safe_drop_tfm)
else:
for i in range(1, 8):
miniseg_traj[lr].append(miniseg_traj[lr][-1])
miniseg_trajs.append(miniseg_traj)
for lr in 'lr':
hmats = np.asarray(miniseg_traj[lr])
handles.append(sim_env.env.drawlinestrip(hmats[:,:3,3], 2, (0,0,1,1)))
redprint("Executing joint trajectory for part %i using arms '%s'"%(i_miniseg, miniseg_traj.keys()))
for lr in 'lr':
gripper_open = lr_open[lr][i_miniseg]
prev_gripper_open = lr_open[lr][i_miniseg-1] if i_miniseg != 0 else False
if not sim_util.set_gripper_maybesim(sim_env, lr, gripper_open, prev_gripper_open, floating=True):
redprint("Grab %s failed"%lr)
success = False
if not success: break
if len(miniseg_traj) > 0:
success &= sim_util.exec_traj_sim(sim_env, miniseg_traj, animate=animate)
if not success: break
sim_env.sim.settle(animate=animate)
sim_env.sim.release_rope('l')
sim_env.sim.release_rope('r')
if animate:
sim_env.viewer.Step()
return success, feasible, misgrasp, miniseg_trajs
def main():
import IPython
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
else:
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
print "got new xyz"
redprint(new_xyz)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
### Old end effector forces at r_gripper_tool_frame (including torques)
old_forces = seg_info['end_effector_forces'][:,0:3,:]
old_torques = seg_info['end_effector_forces'][:,3:6,:]
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
old_xyz_transformed = f.transform_points(old_xyz)
#handles.append(Globals.env.plot3(old_xyz_transformed,5, np.array([(0,0,1,1) for i in old_xyz_transformed])))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'r':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#print old_ee_traj
old_ee_pos = old_ee_traj[:,0:3,3]
#print old_ee_pos
# End effector forces as oppossed to end effector trajectories
dfdxs = f.compute_jacobian(old_ee_pos)
old_eefs = []
new_eefs = []
for i in xrange(len(old_forces)):
dfdx = np.matrix(dfdxs[i])
old_force = np.matrix(old_forces[i])
old_torque = np.matrix(old_torques[i])
new_force = (dfdx * old_force)
new_torque = (dfdx * old_torque)
old_eefs.append(np.vstack((old_force, old_torque)))
new_eefs.append(np.vstack((new_force, new_torque)))
old_eefs = np.asarray(old_eefs)[:,:,0]
new_eefs = np.asarray(new_eefs)[:,:,0]
force_data = {}
force_data['old_eefs'] = old_eefs
force_data['new_eefs'] = new_eefs
force_file = open("trial.pickle", 'wa')
pickle.dump(force_data, force_file)
force_file.close()
new_ee_traj_x = new_ee_traj
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'r':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
#if arm_moved(old_joint_traj): NOT SURE WHY BUT THIS IS RETURNING FALSE
lr2oldtraj[lr] = old_joint_traj
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, lr2oldtraj['r'].shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
# Setting robot arm to joint trajectory
r_vals = lr2oldtraj['r'][i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
#IPython.embed()
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
# FIRST RESAMPLING HAPPENS HERE: JOINT_LENGTH
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP) # Timesteps only, can use to inter eefs for first time
####
new_eefs_segment = asarray(new_eefs[i_start:i_end+1,:]) # Extract miniseg, and re-sample
if args.no_ds:
new_eefs_segment_rs = new_eefs_segment
else:
new_eefs_segment_rs = mu.interp2d(timesteps_rs, np.arange(len(new_eefs_segment)), new_eefs_segment)
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
if args.no_ds:
old_joint_traj_rs = old_joint_traj
new_ee_traj_rs = new_ee_traj
ds_inds = np.arange(0, len(new_ee_traj_rs), args.trajopt_ds)
new_ee_traj_rs = new_ee_traj_rs[ds_inds]
old_joint_traj_rs = old_joint_traj_rs[ds_inds]
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
new_joint_traj = mu.interp2d(np.arange(len(old_joint_traj)), np.arange(0, len(new_joint_traj) * args.trajopt_ds, args.trajopt_ds), new_joint_traj)
else:
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
if args.execution: Globals.pr2.update_rave()
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
redprint("Joint trajectory has length: " + str(len(bodypart2traj[part_name])))
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
redprint("Press enter to set gripper")
raw_input()
#for lr in 'r':
# set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
if args.pid:
if not args.fake_data_segment: # If running on PR2, add initial state as waypoint and rough interpolate
# Add initial position
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.arm_joint_names)
traj_length = bodypart2traj['rarm'].shape[0]
complete_joint_traj = np.zeros((traj_length+1, 7)) # To include initial state as a way point
complete_joint_traj[1:,:] = bodypart2traj['rarm']
complete_joint_traj[0,:] = arm_position
bodypart2traj['rarm'] = complete_joint_traj
# Add initial eff
J = np.matrix(np.resize(np.array(robot_states.call_return_jacobian()), (6, 7))) # Jacobian
eff = robot_states.compute_end_effector_force(J, arm_effort).T
eff = np.array(eff)[0]
init_force = eff[:3]
complete_force_traj = np.zeros((traj_length+1, 3))
complete_force_traj[1:,:] = new_eefs_segment_rs
complete_force_traj[0,:] = init_force
else:
complete_force_traj = new_eefs_segment_rs
# SECOND RESAMPLING HAPPENS HERE: JOINT VELOCITIES
if args.no_ds:
traj = bodypart2traj['rarm']
stamps = asarray(seg_info['stamps'])
times = np.array([stamps[i_end] - stamps[i_start]])
F = complete_force_traj
else:
times, times_up, traj = pr2_trajectories.return_timed_traj(Globals.pr2, bodypart2traj) # use times and times_up to interpolate the second time
F = mu.interp2d(times_up, times, complete_force_traj)
#Globals.robot.SetDOFValues(asarray(fake_seg["joint_states"]["position"][0]))
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, traj.shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj_rs[:,:3,3], 2, (0,0,1,1)))
# Setting robot arm to joint trajectory
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
qs = np.array(np.matrix(traj).T) # 7 x n
qs = np.resize(traj, (1, qs.shape[1]*7))[0] #resize the data to 1x7n (n being the number of steps)
F = np.array(np.matrix(F).T) # 6 x n
F = np.resize(F, (1, F.shape[1]*6))[0] #resize the data to 1x3n
# Controller code
allCs = []
x = np.ones(6) * 1
v = np.ones(6) * 1e-3
a = np.ones(6) * 1e-6
Ct = np.diag(np.hstack((x, v, a)))
num_steps = i_end - i_start + 1
#need to figure out the stamps correctly
stamps = asarray(seg_info['stamps'][i_start:i_end+1])
for t in range(num_steps-1, -1, -1):
allCs.append(Ct)
m = np.ones(6)
Kps = []
Kvs = []
Ks = []
Qt = None
Vs = []
for t in range(num_steps-1, -1, -1):
if Qt is None:
Qt = allCs[t]
else:
Ft = np.zeros((12, 18))
for j in range(12):
Ft[j][j] = 1.0
deltat = abs(stamps[t+1] - stamps[t])
#print(deltat)
for j in range(6):
Ft[j][j+6] = deltat
for j in range(6):
Ft[j+6][j+12] = deltat/m[j]
for j in range(6):
Ft[j][j+12] = ((deltat)**2)/m[j]
Qt = allCs[t] + (np.transpose(Ft).dot(Vs[num_steps-2-t]).dot(Ft))
Qxx = Qt[0:12, 0:12]
Quu = Qt[12:18, 12:18]
Qxu = Qt[0:12, 12:18]
Qux = Qt[12:18, 0:12]
Quuinv = np.linalg.inv(Quu)
Vt = Qxx - Qxu.dot(Quuinv).dot(Qux)
Kt = -1*(Quuinv.dot(Qux))
Ks.append(Kt)
Kps.append(Kt[:, 0:6])
Kvs.append(Kt[:, 6:12])
Vs.append(Vt)
Ks = Ks[::-1]
Kps = Kps[::-1]
Kvs = Kvs[::-1]
JKpJ = []
JKvJ = []
toAddJkpJ = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
toAddJkvJ = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
JacobianOlds = asarray(seg_info["jacobians"][i_start:i_end+1])
for i in range(i_end- i_start + 1):
J = JacobianOlds[i]
psuedoJ = np.linalg.inv(np.transpose(J).dot(J)).dot(np.transpose(J))
#JKpJ.append(np.transpose(J).dot(Kps[i]).dot(J) + toAddJkpJ*0.001)
#JKvJ.append(np.transpose(J).dot(Kvs[i]).dot(J) + toAddJkvJ*0.001)
JKpJ.append(psuedoJ.dot(Kps[i]).dot(J) + toAddJkpJ*0.001)
JKvJ.append(psuedoJ.dot(Kvs[i]).dot(J) + toAddJkvJ*0.001)
if args.useJK:
Kps = []
Kvs = []
for i in range(i_end- i_start + 1):
Kps.append(np.zeros((6,6)))
Kvs.append(np.zeros((6,6)))
else:
JKpJ = []
JKvJ = []
for i in range(i_end- i_start + 1):
JKpJ.append(np.zeros((7,7)))
JKvJ.append(np.zeros((7,7)))
Kps = np.asarray(Kps)
Kvs = np.asarray(Kvs)
JKpJ = np.asarray(JKpJ)
JKvJ = np.asarray(JKvJ)
IPython.embed()
# # Temp Kps and Kvs for testing
"""
toAddJkpJ = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
toAddJkvJ = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
length = complete_force_traj.shape[0]
JKpJ = np.asarray([toAddJkpJ for i in range(length)])
JKpJ = np.resize(JKpJ, (1, 49*length))[0]
JKvJ = np.asarray([toAddJkvJ for i in range(length)])
JKvJ = np.resize(JKvJ, (1, 49*length))[0]
"""
# [traj, Kp, Kv, F, use_force, seconds]
Kps = np.resize(Kps, (1, 36 * num_steps))[0]
Kvs = np.resize(Kvs, (1, 36 * num_steps))[0]
JKpJ = np.resize(JKpJ, (1, 49 * num_steps))[0]
JKvJ = np.resize(JKvJ, (1, 49 * num_steps))[0]
data = np.zeros((1, len(qs) + len(JKpJ) + len(JKvJ) + len(F) + len(Kps) + len(Kvs) + 2))
data[0][0:len(qs)] = qs
data[0][len(qs):len(qs)+len(JKpJ)] = JKpJ
data[0][len(qs)+len(JKpJ):len(qs)+len(JKpJ)+len(JKvJ)] = JKvJ
data[0][len(qs)+len(JKpJ)+len(JKvJ):len(qs)+len(JKpJ)+len(JKvJ)+len(F)] = F
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)] = Kps
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)+len(Kvs)] = Kvs
data[0][-2] = args.use_force
data[0][-1] = stamps[i_end] - stamps[i_start]
msg = Float64MultiArray()
msg.data = data[0].tolist()
pub = rospy.Publisher("/controller_data", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
else:
#if not success: break
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
rospy.init_node("exec_task",disable_signals=True)
global filename
if args.logging:
name = raw_input ('Enter identifier of this experiment (without spaces): ')
name = name + '.yaml'
import os.path as osp
filename = osp.join('/home/sibi/sandbox/rapprentice/experiments/', name)
if not osp.exists(filename):
with open(filename,'w') as fh:
fh.write("experiment: %s\ninfo: \n"%name)
#thc.start()
if args.execution:
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
import trajoptpy.make_kinbodies as mk
#Globals.env.Load("/home/sibi/sandbox/rapprentice/objects/table.xml")
addBoxToRave(Globals.env, "table")
Globals.sponge = addBoxToRave(Globals.env, "sponge") # Not sure about this
Globals.needle_tip = mk.create_spheres(Globals.env, [(0,0,0)], radii=0.02, name="needle_tip")
Globals.demo_env = Globals.env.CloneSelf(1)
Globals.demo_env.StopSimulation()
Globals.demo_robot = Globals.demo_env.GetRobot("pr2")
Globals.demo_needle_tip = Globals.demo_env.GetKinBody("needle_tip")
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
thc.grabber = grabber
#Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
#Globals.env.SetViewer('qtcoin')
#threadClass().start()
while True:
redprint("Acquire point cloud")
################################
redprint("Finding closest demonstration")
if args.fake_data_segment:
seg_name = args.fake_data_segment
else:
seg_name = find_closest(demofile)
seg_info = demofile[seg_name]
# redprint("using demo %s, description: %s"%(seg_name, seg_info["description"]))
################################
redprint("Generating end-effector trajectory")
handles = []
if seg_info.get('ar_marker_poses') is None:
use_markers = False
else:
use_markers = True
if use_markers:
old_marker_poses_str = seg_info['ar_marker_poses']
old_marker_poses = {}
for i in old_marker_poses_str:
old_marker_poses[int(i)] = old_marker_poses_str[i]
# TODO: Have a check for only transformations -> rigid transformations
if args.fake_data_segment:
new_keypoints = demofile[args.fake_data_segment]["key_points"]
if use_markers:
new_marker_poses_str = demofile[args.fake_data_segment]["ar_marker_poses"]
new_marker_poses = {}
for i in new_marker_poses_str:
new_marker_poses[int(i)] = new_marker_poses_str[i]
if seg_info['key_points'].keys().sort() != new_keypoints.keys().sort():
print "Keypoints don't match."
exit(1)
else:
if args.execution: Globals.pr2.update_rave()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_keypoints, new_marker_poses = fk.get_keypoints_execution(grabber, seg_info['key_points'].keys(), T_w_k, use_markers)
print "Warping the points for the new situation."
if "l_grab" in seg_info['extra_information'] or "r_grab" in seg_info['extra_information']:
use_ntt_kp = False
else:
use_ntt_kp = True
# Points from keypoints
old_xyz, rigid, kp_mapping = fk.key_points_to_points(seg_info['key_points'], use_ntt_kp)
new_xyz, _, _ = fk.key_points_to_points(new_keypoints, use_ntt_kp)
# Points from markers
if use_markers:
old_common_poses, new_common_poses = find_common_marker_poses(old_marker_poses, new_marker_poses, new_keypoints.keys())
old_m_xyz, rigid_m, _ = fk.key_points_to_points(old_common_poses, False)
new_m_xyz, _, _ = fk.key_points_to_points(new_common_poses, False)
if len(old_m_xyz) > 0 and rigid: rigid = False
elif rigid_m and not old_xyz.any(): rigid = True
# concatenate points
if old_xyz.any() and old_m_xyz.any():
old_xyz = np.r_[old_xyz, old_m_xyz]
elif old_m_xyz.any():
old_xyz = old_m_xyz
if new_xyz.any() and new_m_xyz.any():
new_xyz = np.r_[new_xyz, new_m_xyz]
elif new_m_xyz.any():
new_xyz = new_m_xyz
if new_xyz.any() and new_xyz.shape != (4,4):
#handles.append(Globals.env.plot3(old_xyz,5, (1,0,0,1)))
#handles.append(Globals.env.plot3(old_xyz,5, np.array([(1,0,0) for _ in xrange(old_xyz.shape[0])])))
handles.append(Globals.env.plot3(new_xyz,5, np.array([(0,0,1) for _ in xrange(old_xyz.shape[0])])))
print 'Old points:', old_xyz
print 'New points:', new_xyz
#if not yes_or_no.yes_or_no("Use identity?"):
if rigid:
f = registration.ThinPlateSpline()
rel_tfm = new_xyz.dot(np.linalg.inv(old_xyz))
f.init_rigid_tfm(rel_tfm)
bend_c = 0
rot_c = 0
scale_c = 0
max_error = None
elif len(new_xyz) > 0:
#f.fit(demopoints_m3, newpoints_m3, 10,10)
# TODO - check if this regularization on bending is correct
#if "right_hole_normal" in new_keypoints or "left_hole_normal" in new_keypoints:
# bend_c = 0.1
# rot_c = [1e-5,1e-5,0.1]
#wt = 5
#wt_n = np.ones(len(old_xyz))
#if kp_mapping.get("right_hole_normal"):
# wt_n[kp_mapping["right_hole_normal"][0]] = wt
#if kp_mapping.get("left_hole_normal"):
# wt_n[kp_mapping["left_hole_normal"][0]] = wt
#else:
# bend_c = 0.1
# rot_c = 1e-5#[0,0,1e-5]
# wt_n = None
# f = registration.fit_ThinPlateSpline(old_xyz, new_xyz, bend_coef = bend_c,rot_coef = rot_c)
bend_c = 0.05
rot_c = [1e-3, 1e-3, 1e-3]
scale_c = 0.1
f = registration.fit_ThinPlateSpline_RotReg(old_xyz, new_xyz, bend_c, rot_c, scale_c)
np.set_printoptions(precision=3)
max_error = np.max(np.abs(f.transform_points(old_xyz) - new_xyz))
print "nonlinear part", f.w_ng
print "affine part", f.lin_ag
print "translation part", f.trans_g
print "residual", f.transform_points(old_xyz) - new_xyz
print "max error ", max_error
else:
f = registration.ThinPlateSpline()
bend_c = 0
rot_c = 0
scale_c = 0
max_error = None
# else:
# f = registration.ThinPlateSpline()
# bend_c = 0
# rot_c = 0
#
if old_xyz.any() and old_xyz.shape != (4,4):
tfm_xyz = f.transform_points(old_xyz)
handles.append(Globals.env.plot3(tfm_xyz,5, np.array([(0,1,0) for _ in xrange(tfm_xyz.shape[0])])))
#f = registration.ThinPlateSpline() XXX XXX
if new_xyz.any() and new_xyz.shape != (4,4):
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0), old_xyz.max(axis=0), xres = .1, yres = .1, zres = .04))
if args.ask:
# if new_xyz.any() and new_xyz.shape != (4,4):
# import visualize
# visualize.plot_mesh_points(f.transform_points, old_xyz, new_xyz)
#lines = plotting_openrave.gen_grid(f.transform_points, np.array([0,-1,0]), np.array([1,1,1]))
#plotting_openrave.plot_lines(lines)
if not yes_or_no.yes_or_no("Continue?"):
continue
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
# Assuming only lgrab or rgrab
use_needle = None
for lr in 'lr':
if "%s_grab"%lr in seg_info['extra_information']:
if "needle_tip_transform" in seg_info['key_points']:
demo_ntfm = np.array(seg_info['key_points']['needle_tip_transform'])
ntfm = np.array(new_keypoints['needle_tip_transform'])
use_needle = lr
elif Globals.rel_ntfm is not None:
T_w_g = Globals.robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
T_g_n = Globals.rel_ntfm
ntfm = T_w_g.dot(T_g_n)
T_w_g_demo = Globals.demo_robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
T_g_n_demo = Globals.demo_rel_ntfm
demo_ntfm = T_w_g_demo.dot(T_g_n_demo)
use_needle = lr
if use_needle is not None:
setup_needle_grabs(use_needle, seg_info["joint_states"]["name"], seg_info["joint_states"]["look_position"], demo_ntfm, ntfm)
if args.logging:
with open(filename,'a') as fh:
fh.write("- seg_name: %s\n"%seg_name)
fh.write(" tps_info: \n")
if max_error is not None:
res_cost, bend_cost, tot_cost = f.fitting_cost(new_xyz, bend_c)
fh.write(" res_cost: %f\n bend_cost: %f\n tot_cost: %f\n"%(res_cost, bend_cost, tot_cost))
fh.write(" max_tps_error: %f\n"%max_error)
else:
fh.write(" res_cost: -1\n bend_cost: -1\n tot_cost: -1\n max_tps_error: -1\n")
fh.write(" trajopt_info: \n")
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution: Globals.pr2.update_rave()
# Changing the trajectory according to the end-effector
full_traj = np.c_[seg_info["leftarm"][i_start:i_end+1], seg_info["rightarm"][i_start:i_end+1]]
full_traj = mu.remove_duplicate_rows(full_traj)
_, ds_traj = resampling.adaptive_resample(full_traj, tol=.01, max_change=.1) # about 2.5 degrees, 10 degrees
Globals.robot.SetActiveDOFs(np.r_[Globals.robot.GetManipulator("leftarm").GetArmIndices(), Globals.robot.GetManipulator("rightarm").GetArmIndices()])
Globals.demo_robot.SetActiveDOFs(np.r_[Globals.robot.GetManipulator("leftarm").GetArmIndices(), Globals.robot.GetManipulator("rightarm").GetArmIndices()])
Globals.demo_robot.SetDOFValues(Globals.robot.GetDOFValues())
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
bodypart2traj = {}
arms_used = ""
if args.logging:
with open(filename, 'a') as fh:
fh.write(" mini_seg%i: \n"%i_miniseg)
for lr in 'lr':
# if "%s_grab"%lr in seg_info['extra_information']:
# if "needle_tip_transform" in seg_info['key_points']:
# demo_ntfm = np.array(seg_info['key_points']['needle_tip_transform'])
# ntfm = np.array(new_keypoints['needle_tip_transform'])
# use_needle = True
# elif Globals.rel_ntfm is not None:
# T_w_g = Globals.robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
# T_g_n = Globals.rel_ntfm
# ntfm = T_w_g.dot(T_g_n)
# T_w_g_demo = Globals.demo_robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
# T_g_n_demo = Globals.demo_rel_ntfm
# demo_ntfm = T_w_g_demo.dot(T_g_n_demo)
# use_needle = True
# else:
# use_needle = False
# else:
# use_needle = False
if use_needle == lr:
Globals.sponge.Enable(False)
arm_traj = {'l':ds_traj[:,:7], 'r':ds_traj[:,7:]}[lr]
old_ee_traj = setup_needle_traj (lr, arm_traj)
link = Globals.needle_tip.GetLinks()[0]
redprint("Using needle for trajectory execution...")
else:
arm = {"l":"leftarm", "r":"rightarm"}[lr]
Globals.demo_robot.SetActiveDOFs(Globals.robot.GetManipulator(arm).GetArmIndices())
if seg_name == "8_knot_tie_next0_seg00":
Globals.sponge.Enable(False)
else:
Globals.sponge.Enable(True)
ee_link_name = "%s_gripper_tool_frame"%lr
link = Globals.robot.GetLink(ee_link_name)
#old_ee_traj = np.asarray(seg_info[ee_link_name]["hmat"])
demo_link = Globals.demo_robot.GetLink(ee_link_name)
old_ee_traj = []
arm_traj = {'l':ds_traj[:,:7], 'r':ds_traj[:,7:]}[lr]
for row in arm_traj:
Globals.demo_robot.SetActiveDOFValues(row)
old_ee_traj.append(demo_link.GetTransform())
############ CAN YOU PLOT THIS OLD_EE_TRAJ?
old_ee_traj = np.asarray(old_ee_traj)
old_joint_traj = {'l':ds_traj[:,:7], 'r':ds_traj[:,7:]}[lr]
if lr == 'l':#arm_moved(old_joint_traj):
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
new_ee_traj = f.transform_hmats(old_ee_traj)
#new_ee_traj = old_ee_traj
#################### CAN YOU PLOT THIS NEW_EE_TRAJ?
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
#old_poses = [conv.hmat_to_pose(hmat) for hmat in old_ee_traj]
#print new_ee_traj
#new_poses = [conv.hmat_to_pose(hmat) for hmat in new_ee_traj]
update_markers(new_ee_traj, old_ee_traj)
#thc.run()
if args.execution: Globals.pr2.update_rave()
new_joint_traj, costs, cnts, pos_err = plan_follow_traj(Globals.robot, manip_name,
link, new_ee_traj, old_joint_traj, True)
new_joint_traj = unwrap_joint_traj (lr, new_joint_traj)
# (robot, manip_name, ee_link, new_hmats, old_traj):
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
arms_used += lr
if args.logging:
with open(filename, 'a') as fh:
fh.write(" - %s: \n"%lr)
fh.write(" costs: \n")
tot_cost = 0
for c_name, c_val in costs:
fh.write(" - %s: %f\n"%(c_name, c_val))
tot_cost += c_val
fh.write(" - tot_cost: %f\n"%tot_cost)
fh.write(" constraints: \n")
for cnt in cnts:
fh.write(" - %s\n"%str(cnt))
fh.write(" max_pos_error: %f\n"%pos_err)
################################
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, arms_used))
for lr in 'lr':
set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
#trajoptpy.GetViewer(Globals.env).Idle()
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj, speed_factor=1)
# TODO measure failure condtions
if not success:
break
Globals.robot.ReleaseAllGrabbed()
Globals.demo_robot.ReleaseAllGrabbed()
redprint("Segment %s result: %s"%(seg_name, success))
if args.fake_data_segment: break
