target_pt_world = cb_points_world[target_ind[0]]
# create robot pose relative to target point
R_gripper_world = np.array([[1.0, 0, 0],
[0, -1.0, 0],
[0, 0, -1.0]])
t_gripper_world = np.array([target_pt_world.x + config['gripper_offset_x'],
target_pt_world.y + config['gripper_offset_y'],
target_pt_world.z + config['gripper_offset_z']])
T_gripper_world = RigidTransform(rotation=R_gripper_world,
translation=t_gripper_world,
from_frame='gripper',
to_frame='cb')
logging.info('Moving robot to point x=%f, y=%f, z=%f' %(t_gripper_world[0], t_gripper_world[1], t_gripper_world[2]))
# move robot to pose
y = YuMiRobot(tcp=YMC.TCP_SUCTION_STIFF)
y.reset_home()
time.sleep(1)
T_lift = RigidTransform(translation=(0,0,0.05), from_frame='cb', to_frame='cb')
T_gripper_world_lift = T_lift * T_gripper_world
y.right.goto_pose(T_gripper_world_lift)
y.right.goto_pose(T_gripper_world)
# wait for human measurement
yesno = raw_input('Take measurement. Hit [ENTER] when done')
y.right.goto_pose(T_gripper_world_lift)
y.reset_home()
y.stop()
def register_ensenso(config):
# set parameters
average = True
add_to_buffer = True
clear_buffer = False
decode = True
grid_spacing = -1
# read parameters
num_patterns = config["num_patterns"]
max_tries = config["max_tries"]
# load tf pattern to world
T_pattern_world = RigidTransform.load(config["pattern_tf"])
# initialize sensor
sensor_frame = config["sensor_frame"]
sensor_config = {"frame": sensor_frame}
logging.info("Creating sensor")
sensor = RgbdSensorFactory.sensor("ensenso", sensor_config)
# initialize node
rospy.init_node("register_ensenso", anonymous=True)
rospy.wait_for_service("/%s/collect_pattern" % (sensor_frame))
rospy.wait_for_service("/%s/estimate_pattern_pose" % (sensor_frame))
# start sensor
print("Starting sensor")
sensor.start()
time.sleep(1)
print("Sensor initialized")
# perform registration
try:
print("Collecting patterns")
num_detected = 0
i = 0
while num_detected < num_patterns and i < max_tries:
collect_pattern = rospy.ServiceProxy(
"/%s/collect_pattern" % (sensor_frame), CollectPattern)
resp = collect_pattern(add_to_buffer, clear_buffer, decode,
grid_spacing)
if resp.success:
print("Detected pattern %d" % (num_detected))
num_detected += 1
i += 1
if i == max_tries:
raise ValueError("Failed to detect calibration pattern!")
print("Estimating pattern pose")
estimate_pattern_pose = rospy.ServiceProxy(
"/%s/estimate_pattern_pose" % (sensor_frame), EstimatePatternPose)
resp = estimate_pattern_pose(average)
q_pattern_camera = np.array([
resp.pose.orientation.w,
resp.pose.orientation.x,
resp.pose.orientation.y,
resp.pose.orientation.z,
])
t_pattern_camera = np.array(
[resp.pose.position.x, resp.pose.position.y, resp.pose.position.z])
T_pattern_camera = RigidTransform(
rotation=q_pattern_camera,
translation=t_pattern_camera,
from_frame="pattern",
to_frame=sensor_frame,
)
except rospy.ServiceException as e:
print("Service call failed: %s" % (str(e)))
# compute final transformation
T_camera_world = T_pattern_world * T_pattern_camera.inverse()
# save final transformation
output_dir = os.path.join(config["calib_dir"], sensor_frame)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
pose_filename = os.path.join(output_dir, "%s_to_world.tf" % (sensor_frame))
T_camera_world.save(pose_filename)
intr_filename = os.path.join(output_dir, "%s.intr" % (sensor_frame))
sensor.ir_intrinsics.save(intr_filename)
# log final transformation
print("Final Result for sensor %s" % (sensor_frame))
print("Rotation: ")
print(T_camera_world.rotation)
print("Quaternion: ")
print(T_camera_world.quaternion)
print("Translation: ")
print(T_camera_world.translation)
# stop sensor
sensor.stop()
# move robot to calib pattern center
if config["use_robot"]:
# create robot pose relative to target point
target_pt_camera = Point(T_pattern_camera.translation, sensor_frame)
target_pt_world = T_camera_world * target_pt_camera
R_gripper_world = np.array([[1.0, 0, 0], [0, -1.0, 0], [0, 0, -1.0]])
t_gripper_world = np.array([
target_pt_world.x + config["gripper_offset_x"],
target_pt_world.y + config["gripper_offset_y"],
target_pt_world.z + config["gripper_offset_z"],
])
T_gripper_world = RigidTransform(
rotation=R_gripper_world,
translation=t_gripper_world,
from_frame="gripper",
to_frame="world",
)
# move robot to pose
y = YuMiRobot(tcp=YMC.TCP_SUCTION_STIFF)
y.reset_home()
time.sleep(1)
T_lift = RigidTransform(translation=(0, 0, 0.05),
from_frame="world",
to_frame="world")
T_gripper_world_lift = T_lift * T_gripper_world
y.right.goto_pose(T_gripper_world_lift)
y.right.goto_pose(T_gripper_world)
# wait for human measurement
keyboard_input("Take measurement. Hit [ENTER] when done")
y.right.goto_pose(T_gripper_world_lift)
y.reset_home()
y.stop()
def playback(args):
cfg = YamlConfig(args.config_path)
demo_name = args.demo_name
supervisor = args.supervisor
trial_num = args.trial_num
if cfg['mode'] not in ('poses', 'states'):
y.stop()
raise ValueError("Unknown playback mode! Only accepts 'poses' or 'joints'. Got {0}".format(cfg['mode']))
# init robot
logging.info("Init robot.")
y = YuMiRobot()
y.set_v(cfg['v'])
y.set_z(cfg['z'])
# load demo data
demo_records = CSVModel.load(os.path.join(cfg['data_path'], 'demo_records.csv'))
demo_record = demo_records.get_by_cols({
'demo_name': demo_name,
'trial_num': trial_num,
'supervisor': supervisor
})
trial_path = demo_record['trial_path']
demo_host_cfg = YamlConfig(os.path.join(trial_path, 'demo_config.yaml'))
# parse demo trajectory
# TODO: enforce fps
fps = demo_host_cfg['fps']
_, left_data = zip(*load(os.path.join(trial_path, '{0}_left.jb'.format(cfg['mode']))))
_, right_data = zip(*load(os.path.join(trial_path, '{0}_right.jb'.format(cfg['mode']))))
_, gripper_left_evs = zip(*load(os.path.join(trial_path, 'grippers_evs_left.jb')))
_, gripper_right_evs = zip(*load(os.path.join(trial_path, 'grippers_evs_right.jb')))
seqs = {
'left': Sequence([t[1] for t in left_data]),
'right': Sequence([t[1] for t in right_data]),
'gripper_left': Sequence(gripper_left_evs),
'gripper_right': Sequence(gripper_right_evs)
}
# subsampling
subsample_factor = cfg['subsample']
subsampled_seqs = {
'left': seqs['left'].subsampler(subsample_factor),
'right': seqs['right'].subsampler(subsample_factor),
'gripper_left': seqs['gripper_left'].subsampler(subsample_factor, retain_features=True),
'gripper_right': seqs['gripper_right'].subsampler(subsample_factor, retain_features=True)
}
# concating non-moving steps
if cfg['concat']:
concat_data = {
'left': [subsampled_seqs['left'].data[0]],
'right': [subsampled_seqs['right'].data[0]],
'gripper_left': [subsampled_seqs['gripper_left'].data[0]],
'gripper_right': [subsampled_seqs['gripper_right'].data[0]]
}
last_lp = concat_data['left'][0]
last_rp = concat_data['right'][0]
for t in range(1, min([len(seq.data) for seq in subsampled_seqs.values()])):
lg_t = subsampled_seqs['gripper_left'].data[t]
rg_t = subsampled_seqs['gripper_right'].data[t]
lp_t = subsampled_seqs['left'].data[t]
rp_t = subsampled_seqs['right'].data[t]
if lg_t is not None or rg_t is not None or \
lp_t != last_lp or rp_t != last_rp:
concat_data['gripper_right'].append(rg_t)
concat_data['gripper_left'].append(lg_t)
concat_data['left'].append(lp_t)
concat_data['right'].append(rp_t)
last_lp = lp_t
last_rp = rp_t
concat_seqs = {
'left': Sequence(concat_data['left']),
'right': Sequence(concat_data['right']),
'gripper_left': Sequence(concat_data['gripper_left']),
'gripper_right': Sequence(concat_data['gripper_right']),
}
else:
concat_seqs = subsampled_seqs
N = min([len(seq.data) for seq in concat_seqs.values()])
# processing time steps where zoning should be set to fine
gripper_zoning = [None for _ in range(N)]
for t in range(N-1):
if concat_seqs['gripper_left'].data[t] != None or \
concat_seqs['gripper_right'].data[t] != None:
if t == 0:
y.set_z('fine')
else:
gripper_zoning[t-1] = 'fine'
gripper_zoning[t+1] = cfg['z']
# perform setup motions
logging.info("Loading demo and performing setups.")
y.reset_home()
y.open_grippers()
demo_path = os.path.join(trial_path, '{0}.py'.format(demo_name))
demo_obj = DemoWrapper.load(demo_path, y)
demo_obj.setup()
# record torque and other debug data if needed
if cfg['record_torque']['use']:
ysub = YuMiSubscriber()
ysub.start()
data_torque_left = DataStreamRecorder('torques_left', ysub.left.get_torque, cache_path=cfg['cache_path'], save_every=cfg['save_every'])
data_torque_right = DataStreamRecorder('torques_right', ysub.right.get_torque, cache_path=cfg['cache_path'], save_every=cfg['save_every'])
syncer = DataStreamSyncer([data_torque_left, data_torque_right], fps)
syncer.start()
sleep(0.5)
syncer.pause()
syncer.flush()
syncer.resume(reset_time=True)
# perform trajectory
logging.info("Playing trajectory")
for t in range(N):
left_item = concat_seqs['left'].data[t]
right_item = concat_seqs['right'].data[t]
gripper_left_item = concat_seqs['gripper_left'].data[t]
gripper_right_item = concat_seqs['gripper_right'].data[t]
if cfg['mode'] == 'poses':
y.left.goto_pose(left_item, relative=True, wait_for_res=False)
y.right.goto_pose(right_item, relative=True, wait_for_res=True)
else:
y.left.goto_state(left_item, wait_for_res=False)
y.right.goto_state(right_item, wait_for_res=True)
if gripper_left_item != None and gripper_right_item != None:
getattr(y.left, gripper_left_item)(wait_for_res=False)
getattr(y.right, gripper_right_item)(wait_for_res=True)
elif gripper_left_item != None:
getattr(y.left, gripper_left_item)()
elif gripper_right_item != None:
getattr(y.right, gripper_right_item)()
z = gripper_zoning[t]
if z is not None:
logging.info("Setting zone to {0}".format(z))
y.set_z(z)
if cfg['record_torque']['use']:
syncer.pause()
torque_model = CSVModel.get_or_create(
os.path.join(cfg['data_path'], 'playback_torques_record.csv'),
[
('supervisor', 'str'),
('demo_name', 'str'),
('trial_num', 'int'),
('playback_num', 'int'),
('playback_path', 'str')
]
)
last_torque_record = torque_model.get_by_cols({
'demo_name': demo_name,
'trial_num': trial_num,
'supervisor': supervisor
}, direction=-1)
if last_torque_record == None:
playback_num = 1
else:
playback_num = last_torque_record['playback_num'] + 1
playback_path = os.path.join(trial_path, 'playback_torques', str(playback_num))
if not os.path.exists(playback_path):
os.makedirs(playback_path)
data_torque_left.save_data(playback_path)
data_torque_right.save_data(playback_path)
torque_model.insert({
'supervisor': supervisor,
'demo_name': demo_name,
'trial_num': trial_num,
'playback_num': playback_num,
'playback_path': playback_path
})
basename = os.path.basename(args.config_path)
target_file_path = os.path.join(playback_path, basename)
shutil.copyfile(args.config_path, target_file_path)
ysub.stop()
syncer.stop()
# perform takedown motions
logging.info("Taking down..")
y.set_v(cfg['v'])
y.set_z(cfg['z'])
demo_obj.takedown()
y.reset_home()
y.open_grippers()
y.stop()
def playback(args):
cfg = YamlConfig(args.config_path)
demo_name = args.demo_name
supervisor = args.supervisor
trial_num = args.trial_num
if cfg['mode'] not in ('poses', 'states'):
y.stop()
raise ValueError(
"Unknown playback mode! Only accepts 'poses' or 'joints'. Got {0}".
format(cfg['mode']))
# init robot
logging.info("Init robot.")
y = YuMiRobot()
y.set_v(cfg['v'])
y.set_z(cfg['z'])
# load demo data
demo_records = CSVModel.load(
os.path.join(cfg['data_path'], 'demo_records.csv'))
demo_record = demo_records.get_by_cols({
'demo_name': demo_name,
'trial_num': trial_num,
'supervisor': supervisor
})
trial_path = demo_record['trial_path']
demo_host_cfg = YamlConfig(os.path.join(trial_path, 'demo_config.yaml'))
# parse demo trajectory
# TODO: enforce fps
fps = demo_host_cfg['fps']
_, left_data = zip(
*load(os.path.join(trial_path, '{0}_left.jb'.format(cfg['mode']))))
_, right_data = zip(
*load(os.path.join(trial_path, '{0}_right.jb'.format(cfg['mode']))))
_, gripper_left_evs = zip(
*load(os.path.join(trial_path, 'grippers_evs_left.jb')))
_, gripper_right_evs = zip(
*load(os.path.join(trial_path, 'grippers_evs_right.jb')))
seqs = {
'left': Sequence([t[1] for t in left_data]),
'right': Sequence([t[1] for t in right_data]),
'gripper_left': Sequence(gripper_left_evs),
'gripper_right': Sequence(gripper_right_evs)
}
# subsampling
subsample_factor = cfg['subsample']
subsampled_seqs = {
'left':
seqs['left'].subsampler(subsample_factor),
'right':
seqs['right'].subsampler(subsample_factor),
'gripper_left':
seqs['gripper_left'].subsampler(subsample_factor,
retain_features=True),
'gripper_right':
seqs['gripper_right'].subsampler(subsample_factor,
retain_features=True)
}
# concating non-moving steps
if cfg['concat']:
concat_data = {
'left': [subsampled_seqs['left'].data[0]],
'right': [subsampled_seqs['right'].data[0]],
'gripper_left': [subsampled_seqs['gripper_left'].data[0]],
'gripper_right': [subsampled_seqs['gripper_right'].data[0]]
}
last_lp = concat_data['left'][0]
last_rp = concat_data['right'][0]
for t in range(
1, min([len(seq.data) for seq in subsampled_seqs.values()])):
lg_t = subsampled_seqs['gripper_left'].data[t]
rg_t = subsampled_seqs['gripper_right'].data[t]
lp_t = subsampled_seqs['left'].data[t]
rp_t = subsampled_seqs['right'].data[t]
if lg_t is not None or rg_t is not None or \
lp_t != last_lp or rp_t != last_rp:
concat_data['gripper_right'].append(rg_t)
concat_data['gripper_left'].append(lg_t)
concat_data['left'].append(lp_t)
concat_data['right'].append(rp_t)
last_lp = lp_t
last_rp = rp_t
concat_seqs = {
'left': Sequence(concat_data['left']),
'right': Sequence(concat_data['right']),
'gripper_left': Sequence(concat_data['gripper_left']),
'gripper_right': Sequence(concat_data['gripper_right']),
}
else:
concat_seqs = subsampled_seqs
N = min([len(seq.data) for seq in concat_seqs.values()])
# processing time steps where zoning should be set to fine
gripper_zoning = [None for _ in range(N)]
for t in range(N - 1):
if concat_seqs['gripper_left'].data[t] != None or \
concat_seqs['gripper_right'].data[t] != None:
if t == 0:
y.set_z('fine')
else:
gripper_zoning[t - 1] = 'fine'
gripper_zoning[t + 1] = cfg['z']
# perform setup motions
logging.info("Loading demo and performing setups.")
y.reset_home()
y.open_grippers()
demo_path = os.path.join(trial_path, '{0}.py'.format(demo_name))
demo_obj = DemoWrapper.load(demo_path, y)
demo_obj.setup()
# record torque and other debug data if needed
if cfg['record_torque']['use']:
ysub = YuMiSubscriber()
ysub.start()
data_torque_left = DataStreamRecorder('torques_left',
ysub.left.get_torque,
cache_path=cfg['cache_path'],
save_every=cfg['save_every'])
data_torque_right = DataStreamRecorder('torques_right',
ysub.right.get_torque,
cache_path=cfg['cache_path'],
save_every=cfg['save_every'])
syncer = DataStreamSyncer([data_torque_left, data_torque_right], fps)
syncer.start()
sleep(0.5)
syncer.pause()
syncer.flush()
syncer.resume(reset_time=True)
# perform trajectory
logging.info("Playing trajectory")
for t in range(N):
left_item = concat_seqs['left'].data[t]
right_item = concat_seqs['right'].data[t]
gripper_left_item = concat_seqs['gripper_left'].data[t]
gripper_right_item = concat_seqs['gripper_right'].data[t]
if cfg['mode'] == 'poses':
y.left.goto_pose(left_item, relative=True, wait_for_res=False)
y.right.goto_pose(right_item, relative=True, wait_for_res=True)
else:
y.left.goto_state(left_item, wait_for_res=False)
y.right.goto_state(right_item, wait_for_res=True)
if gripper_left_item != None and gripper_right_item != None:
getattr(y.left, gripper_left_item)(wait_for_res=False)
getattr(y.right, gripper_right_item)(wait_for_res=True)
elif gripper_left_item != None:
getattr(y.left, gripper_left_item)()
elif gripper_right_item != None:
getattr(y.right, gripper_right_item)()
z = gripper_zoning[t]
if z is not None:
logging.info("Setting zone to {0}".format(z))
y.set_z(z)
if cfg['record_torque']['use']:
syncer.pause()
torque_model = CSVModel.get_or_create(
os.path.join(cfg['data_path'], 'playback_torques_record.csv'),
[('supervisor', 'str'), ('demo_name', 'str'), ('trial_num', 'int'),
('playback_num', 'int'), ('playback_path', 'str')])
last_torque_record = torque_model.get_by_cols(
{
'demo_name': demo_name,
'trial_num': trial_num,
'supervisor': supervisor
},
direction=-1)
if last_torque_record == None:
playback_num = 1
else:
playback_num = last_torque_record['playback_num'] + 1
playback_path = os.path.join(trial_path, 'playback_torques',
str(playback_num))
if not os.path.exists(playback_path):
os.makedirs(playback_path)
data_torque_left.save_data(playback_path)
data_torque_right.save_data(playback_path)
torque_model.insert({
'supervisor': supervisor,
'demo_name': demo_name,
'trial_num': trial_num,
'playback_num': playback_num,
'playback_path': playback_path
})
basename = os.path.basename(args.config_path)
target_file_path = os.path.join(playback_path, basename)
shutil.copyfile(args.config_path, target_file_path)
ysub.stop()
syncer.stop()
# perform takedown motions
logging.info("Taking down..")
y.set_v(cfg['v'])
y.set_z(cfg['z'])
demo_obj.takedown()
y.reset_home()
y.open_grippers()
y.stop()
def register_ensenso(config):
# set parameters
average = True
add_to_buffer = True
clear_buffer = False
decode = True
grid_spacing = -1
# read parameters
num_patterns = config['num_patterns']
max_tries = config['max_tries']
# load tf pattern to world
T_pattern_world = RigidTransform.load(config['pattern_tf'])
# initialize sensor
sensor_frame = config['sensor_frame']
sensor = EnsensoSensor(sensor_frame)
# initialize node
rospy.init_node('register_ensenso', anonymous=True)
rospy.wait_for_service('/%s/collect_pattern' %(sensor_frame))
rospy.wait_for_service('/%s/estimate_pattern_pose' %(sensor_frame))
# start sensor
print('Starting sensor')
sensor.start()
time.sleep(1)
print('Sensor initialized')
# perform registration
try:
print('Collecting patterns')
num_detected = 0
i = 0
while num_detected < num_patterns and i < max_tries:
collect_pattern = rospy.ServiceProxy('/%s/collect_pattern' %(sensor_frame), CollectPattern)
resp = collect_pattern(add_to_buffer,
clear_buffer,
decode,
grid_spacing)
if resp.success:
print('Detected pattern %d' %(num_detected))
num_detected += 1
i += 1
if i == max_tries:
raise ValueError('Failed to detect calibration pattern!')
print('Estimating pattern pose')
estimate_pattern_pose = rospy.ServiceProxy('/%s/estimate_pattern_pose' %(sensor_frame), EstimatePatternPose)
resp = estimate_pattern_pose(average)
q_pattern_camera = np.array([resp.pose.orientation.w,
resp.pose.orientation.x,
resp.pose.orientation.y,
resp.pose.orientation.z])
t_pattern_camera = np.array([resp.pose.position.x,
resp.pose.position.y,
resp.pose.position.z])
T_pattern_camera = RigidTransform(rotation=q_pattern_camera,
translation=t_pattern_camera,
from_frame='pattern',
to_frame=sensor_frame)
except rospy.ServiceException as e:
print('Service call failed: %s' %(str(e)))
# compute final transformation
T_camera_world = T_pattern_world * T_pattern_camera.inverse()
# save final transformation
output_dir = os.path.join(config['calib_dir'], sensor_frame)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
pose_filename = os.path.join(output_dir, '%s_to_world.tf' %(sensor_frame))
T_camera_world.save(pose_filename)
intr_filename = os.path.join(output_dir, '%s.intr' %(sensor_frame))
sensor.ir_intrinsics.save(intr_filename)
# log final transformation
print('Final Result for sensor %s' %(sensor_frame))
print('Rotation: ')
print(T_camera_world.rotation)
print('Quaternion: ')
print(T_camera_world.quaternion)
print('Translation: ')
print(T_camera_world.translation)
# stop sensor
sensor.stop()
# move robot to calib pattern center
if config['use_robot']:
# create robot pose relative to target point
target_pt_camera = Point(T_pattern_camera.translation, sensor_frame)
target_pt_world = T_camera_world * target_pt_camera
R_gripper_world = np.array([[1.0, 0, 0],
[0, -1.0, 0],
[0, 0, -1.0]])
t_gripper_world = np.array([target_pt_world.x + config['gripper_offset_x'],
target_pt_world.y + config['gripper_offset_y'],
target_pt_world.z + config['gripper_offset_z']])
T_gripper_world = RigidTransform(rotation=R_gripper_world,
translation=t_gripper_world,
from_frame='gripper',
to_frame='world')
# move robot to pose
y = YuMiRobot(tcp=YMC.TCP_SUCTION_STIFF)
y.reset_home()
time.sleep(1)
T_lift = RigidTransform(translation=(0,0,0.05), from_frame='world', to_frame='world')
T_gripper_world_lift = T_lift * T_gripper_world
y.right.goto_pose(T_gripper_world_lift)
y.right.goto_pose(T_gripper_world)
# wait for human measurement
yesno = raw_input('Take measurement. Hit [ENTER] when done')
y.right.goto_pose(T_gripper_world_lift)
y.reset_home()
y.stop()