def run_main():
fa = FrankaArm()
fa.reset_pose()
fa.reset_joints()
magnetic_calibration = MagneticCalibration()
fa.apply_effector_forces_torques(45, 0, 0, 0)
magnetic_calibration.calibrate()
import numpy as np
from frankapy import FrankaArm, SensorDataMessageType
from frankapy import FrankaConstants as FC
from frankapy.proto_utils import sensor_proto2ros_msg, make_sensor_group_msg
from frankapy.proto import JointPositionSensorMessage, ShouldTerminateSensorMessage
from franka_interface_msgs.msg import SensorDataGroup
from frankapy.utils import min_jerk
import rospy
if __name__ == "__main__":
fa = FrankaArm()
fa.reset_joints()
rospy.loginfo('Generating Trajectory')
joints_0 = fa.get_joints()
p = fa.get_pose()
p.translation[2] -= 0.2
fa.goto_pose(p)
joints_1 = fa.get_joints()
T = 5
dt = 0.02
ts = np.arange(0, T, dt)
joints_traj = [min_jerk(joints_1, joints_0, t, T) for t in ts]
rospy.loginfo('Initializing Sensor Publisher')
pub = rospy.Publisher(FC.DEFAULT_SENSOR_PUBLISHER_TOPIC,
SensorDataGroup,
"""
parser = argparse.ArgumentParser()
parser.add_argument('--pose_dmp_weights_path', '-w', type=str, default='')
parser.add_argument('--time', '-t', type=float, default=10)
args = parser.parse_args()
gains = [2000, 2000, 2000, 1500, 1500, 1000, 1000] #joint impedances
# load the dmp weights
if not args.pose_dmp_weights_path:
args.pose_dmp_weights_path = './data/dmp/IROS2020_dmp_data_goal_position_weights.pkl'
pose_dmp_file = open(args.pose_dmp_weights_path, "rb")
pose_dmp_info = pickle.load(pose_dmp_file)
print('Starting robot')
fa = FrankaArm()
# Reset robot pose and joints
print('Opening Grippers')
fa.goto_gripper(0.08, grasp=False)
print('Reset with pose')
fa.reset_pose(duration=10)
print('Reset with joints')
fa.reset_joints()
# instantiate object for AR tag detection
frank = realpeginsert.Robot()
# get transforms
tag2worldTF = frank.detect_ar_world_pos(straighten=True,
shape_class=shapes.Circle,
logging.getLogger().setLevel(logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument(
'--cfg',
'-c',
type=str,
default=
'/home/stevenl3/Documents/planorparam/scripts/real_robot/april_tag_pick_place_azure_kinect_cfg.yaml'
)
parser.add_argument('--no_grasp', '-ng', action='store_true')
args = parser.parse_args()
cfg = YamlConfig(args.cfg)
T_camera_world = RigidTransform.load(cfg['T_k4a_franka_path'])
logging.info('Starting robot')
fa = FrankaArm()
# fa.reset_joints()
# fa.reset_pose()
# fa.open_gripper()
T_ready_world = fa.get_pose()
T_ready_world.translation[0] += 0.25
T_ready_world.translation[2] = 0.4
#ret = fa.goto_pose(T_ready_world)
logging.info('Init camera')
#sensor = get_first_realsense_sensor(cfg['rs']) #original
sensor = Kinect2SensorFactory.sensor('bridged', cfg) #Kinect sensor object
if __name__ == "__main__":
logging.getLogger().setLevel(logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument('--cfg',
'-c',
type=str,
default='cfg/examples/april_tag_pick_place_cfg.yaml')
parser.add_argument('--no_grasp', '-ng', action='store_true')
args = parser.parse_args()
cfg = YamlConfig(args.cfg)
T_camera_ee = RigidTransform.load(cfg['T_camera_ee_path'])
T_camera_mount_delta = RigidTransform.load(cfg['T_camera_mount_path'])
logging.info('Starting robot')
fa = FrankaArm()
fa.set_tool_delta_pose(T_camera_mount_delta)
fa.reset_joints()
fa.open_gripper()
T_ready_world = fa.get_pose()
T_ready_world.translation[0] += 0.25
T_ready_world.translation[2] = 0.4
ret = fa.goto_pose(T_ready_world)
logging.info('Init camera')
sensor = get_first_realsense_sensor(cfg['rs'])
sensor.start()
logging.info('Detecting April Tags')
import numpy as np
from frankapy import FrankaArm
from autolab_core import RigidTransform
if __name__ == '__main__':
print('Starting robot')
fa = FrankaArm()
fa.reset_pose()
fa.reset_joints()
print('Opening Grippers')
fa.open_gripper()
# random_position = RigidTransform(rotation=np.array([
# [1, 0, 0],
# [0, -1, 0],
# [0, 0, -1]
# ]), translation=np.array([0.3069, 0, 0.2867]),
# from_frame='franka_tool', to_frame='world')
# fa.goto_pose_with_cartesian_control(random_position, 10)
fa.apply_effector_forces_torques(10, 0, 0, 0)
import time import numpy as np from frankapy import FrankaArm fa = FrankaArm() time.sleep(6) fa.open_gripper()
from frankapy import FrankaArm
import numpy as np
fa = FrankaArm()
for i in range(20):
input("Prepare to move arm")
fa.apply_effector_forces_torques(3, 0, 0, 0)
rt = fa.get_pose()
print(rt)
from autolab_core import RigidTransform, Point
from frankapy import FrankaArm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--pose_dmp_weights_file_path', type=str, default='')
args = parser.parse_args()
if not args.pose_dmp_weights_file_path:
args.pose_dmp_weights_file_path = '/home/sony/logs/robot_state_data_0_pose_weights.pkl'
pose_dmp_file = open(args.pose_dmp_weights_file_path, "rb")
pose_dmp_info = pickle.load(pose_dmp_file)
print('Starting robot')
fa = FrankaArm()
fa.reset_pose()
fa.reset_joints()
print('Opening Grippers')
fa.open_gripper()
# random_position = RigidTransform(rotation=np.array([
# [1, 0, 0],
# [0, -1, 0],
# [0, 0, -1]
# ]), translation=np.array([0.3069, 0, 0.2867]),
# from_frame='franka_tool', to_frame='world')
import argparse
from frankapy import FrankaArm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--time', '-t', type=float, default=100)
parser.add_argument('--open_gripper', '-o', action='store_true')
args = parser.parse_args()
print('Starting robot')
fa = FrankaArm()
if args.open_gripper:
fa.open_gripper()
print('Applying 0 force torque control for {}s'.format(args.time))
fa.run_guide_mode(args.time)
def run_main():
parser = argparse.ArgumentParser()
parser.add_argument('--filename', type=str, default=None)
args = parser.parse_args()
fa = FrankaArm()
fa.open_gripper()
# fa.reset_pose()
fa.reset_joints(10)
initial_magnet_position1 = RigidTransform(
rotation=np.array([[0, -1, 0], [0, 0, 1], [-1, 0, 0]]),
translation=np.array([0.38592997, 0.10820438, 0.08264024]),
from_frame='franka_tool',
to_frame='world')
initial_magnet_position2 = RigidTransform(
rotation=np.array([[0, -1, 0], [0, 0, 1], [-1, 0, 0]]),
translation=np.array([0.38592997, 0.20820438, 0.08264024]),
from_frame='franka_tool',
to_frame='world')
squeeze_position1 = RigidTransform(
rotation=np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]),
translation=np.array([0.54900978, 0.20820438, 0.20654183]),
from_frame='franka_tool',
to_frame='world')
squeeze_position2 = RigidTransform(
rotation=np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]),
translation=np.array([0.54900978, 0.20820438, 0.15654183]),
from_frame='franka_tool',
to_frame='world')
relative_pos_dist_z = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.0, 0.1]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_pos_dist_y = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.1, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_rotation_z = RigidTransform(rotation=np.array([[0, 1, 0],
[-1, 0, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_rotation_z = RigidTransform(rotation=np.array([[0, -1, 0],
[1, 0, 0],
[0, 0, 1]]),
translation=np.array(
[0.0, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_y = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, -0.1,
0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_z = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.0,
-0.1]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_pos_dist_x = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([0.075, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_x = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([-0.03, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
fa.goto_pose_with_cartesian_control(initial_magnet_position1)
fa.goto_pose_with_cartesian_control(initial_magnet_position2)
magnetic_calibration = MagneticCalibration()
filename = '../calibration/2020-01-14 11-08 E1 2X Board Calibration.npz'
magnetic_calibration.load_calibration_file(filename)
# Close the gripper and save corresponding robot state, gripper state, and magnetic state data
magnetic_calibration.start_recording_data()
time.sleep(1)
# fa.goto_pose_delta_with_cartesian_control(relative_x_dist, 10)
# max width is about 0.080 m
gripper_step_size = 0.002 # in meters
num_samples = 30
noise_level = 13 # uT
force_threshold = 1.05
GRIPPER_CONTACT = False
min_contact = None
min_gripper_width = None
global_min = None
current_force_estimate = 1
while (GRIPPER_CONTACT == False):
current_width = magnetic_calibration.get_current_gripper_width()
print(current_width)
fa.goto_gripper(current_width - gripper_step_size)
#magnetic_calibration.get_last_magnetic_data()
mag_data = magnetic_calibration.get_previous_magnetic_samples(
num_samples) # grab last ten samples
xyz_mag_data = mag_data[:, 6]
print(xyz_mag_data)
#slope = np.diff(xyz_mag_data, axis=0)
#print(slope)
# asign = np.sign(slope)
# signchange = ((np.roll(asign, 1) - asign) != 0).astype(int)
# print(signchange)
# first make sure the signal changes are large enough to not just be noise jitter
# then see if there is a signal change in slope - this signifies a contact.
#if(np.any(abs(np.diff(mag_data)>noise_level))):
#if(abs(np.sum(np.sign(np.diff(mag_data)))<numSamples) & abs(np.diff(mag_data))>noise_level):
if global_min is None:
global_min = np.min(xyz_mag_data, axis=0)
elif global_min > np.min(xyz_mag_data, axis=0):
global_min = np.min(xyz_mag_data, axis=0)
if xyz_mag_data[-1] - global_min > noise_level:
GRIPPER_CONTACT = True
min_gripper_width = current_width - 2 * gripper_step_size
print(min_gripper_width)
print("Min Magnetic Values: ")
print(min_contact)
gripper_step_size -= 0.00003
# if (np.any(np.logical_and((abs(slope)>noise_level), (slope > 0)))):
# # if last value is negative, contact. if last value is positive, release.
# # save max value so we can compare current value and strength of grip
# GRIPPER_CONTACT = True
# min_contact = np.min(xyz_mag_data, axis=0)
# min_gripper_width = current_width-gripper_step_size
# print(min_gripper_width)
# print("Min Magnetic Values: ")
# print(min_contact)
#input("Press enter to continue to next step")
#magnetic_calibration.close_gripper_magnetic_feedback()
# current_width = min_gripper_width
# num_samples = 100
# while(current_force_estimate < force_threshold):
# current_width -= gripper_step_size
# print(current_width)
# fa.goto_gripper(current_width)
# mag_data = magnetic_calibration.get_previous_magnetic_samples(num_samples) # grab last ten samples
# z_mag_data = np.max(mag_data[:,6])
# print(z_mag_data)
# current_force_estimate = global_min / z_mag_data
# print(current_force_estimate)
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_z)
current_joints = fa.get_joints()
current_joints[6] -= math.pi
fa.goto_joints(list(current_joints))
current_position = fa.get_pose()
fa.goto_pose_with_cartesian_control(squeeze_position1)
fa.goto_pose_with_cartesian_control(squeeze_position2)
for i in range(3):
for j in range(i + 1):
fa.goto_gripper(0.0, force=20)
fa.goto_gripper(min_gripper_width)
if i < 2:
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_x)
fa.goto_pose_with_cartesian_control(current_position, 5)
current_joints = fa.get_joints()
current_joints[6] += math.pi
fa.goto_joints(list(current_joints))
fa.goto_pose_delta_with_cartesian_control(relative_neg_dist_z)
fa.open_gripper()
fa.goto_pose_with_cartesian_control(initial_magnet_position1)
#fa.goto_pose_delta_with_cartesian_control(relative_neg_dist_z)
magnetic_calibration.stop_recording_data()
#fa.open_gripper()
print(magnetic_calibration.magnetic_data[0, :])
if args.filename is not None:
magnetic_calibration.saveData(args.filename, min_contact,
min_gripper_width)
magnetic_calibration.plotData(magnetic_calibration.magnetic_data,
'Raw Data Over Time')
magnetic_calibration.plotGripperData(
magnetic_calibration.gripper_state_data, 'Raw Data Over Time')
magnetic_calibration.plotGripperData(magnetic_calibration.robot_state_data,
'Raw Data Over Time')
from frankapy import FrankaArm
if __name__ == '__main__':
fa = FrankaArm()
fa.reset_joints()
fa.open_gripper()
def run_main():
parser = argparse.ArgumentParser()
parser.add_argument('--filename', type=str, default=None)
args = parser.parse_args()
fa = FrankaArm()
fa.open_gripper()
# fa.reset_pose()
fa.reset_joints(10)
pipette_rotation = np.array([[1, 0, 0], [0, -1, 0], [0, 0, -1]])
initial_magnet_position1 = RigidTransform(
rotation=pipette_rotation,
translation=np.array([0.45683638, 0.06513334, 0.20451204]),
from_frame='franka_tool',
to_frame='world')
initial_magnet_position2 = RigidTransform(
rotation=pipette_rotation,
translation=np.array([0.45683638, 0.06513334, 0.15451204]),
from_frame='franka_tool',
to_frame='world')
initial_magnet_position3 = RigidTransform(
rotation=pipette_rotation,
translation=np.array([0.45683638, 0.06513334, 0.33451204]),
from_frame='franka_tool',
to_frame='world')
beaker_position1 = RigidTransform(rotation=pipette_rotation,
translation=np.array(
[0.44724006, 0.2870516, 0.33210899]),
from_frame='franka_tool',
to_frame='world')
beaker_position2 = RigidTransform(rotation=pipette_rotation,
translation=np.array(
[0.44724006, 0.2870516, 0.16210899]),
from_frame='franka_tool',
to_frame='world')
test_tube_1 = RigidTransform(rotation=pipette_rotation,
translation=np.array(
[0.54359048, 0.17224207, 0.19106597]),
from_frame='franka_tool',
to_frame='world')
test_tube_2 = RigidTransform(rotation=pipette_rotation,
translation=np.array(
[0.56359048, 0.17224207, 0.19106597]),
from_frame='franka_tool',
to_frame='world')
test_tube_3 = RigidTransform(rotation=pipette_rotation,
translation=np.array(
[0.58359048, 0.17224207, 0.19106597]),
from_frame='franka_tool',
to_frame='world')
relative_pos_dist_z = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([0.0, 0.0, 0.175]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_pos_dist_y = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.02,
0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_rotation_z = RigidTransform(rotation=np.array([[0, 1, 0],
[-1, 0, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_rotation_z = RigidTransform(rotation=np.array([[0, -1, 0],
[1, 0, 0],
[0, 0, 1]]),
translation=np.array(
[0.0, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_y = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, -0.1,
0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_z = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([0.0, 0.0, -0.175]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_pos_dist_x = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([0.075, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_x = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([-0.03, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
fa.goto_pose_with_cartesian_control(initial_magnet_position1)
fa.goto_pose_with_cartesian_control(initial_magnet_position2)
magnetic_calibration = MagneticCalibration()
filename = '../calibration/2020-01-14 11-08 E1 2X Board Calibration.npz'
magnetic_calibration.load_calibration_file(filename)
# Close the gripper and save corresponding robot state, gripper state, and magnetic state data
magnetic_calibration.start_recording_data()
time.sleep(1)
# fa.goto_pose_delta_with_cartesian_control(relative_x_dist, 10)
# max width is about 0.080 m
gripper_step_size = 0.005 # in meters
num_samples = 30
noise_level = 15 # uT
force_threshold = 1.05
GRIPPER_CONTACT = False
min_contact = None
min_gripper_width = None
global_max = None
current_force_estimate = 1
while (GRIPPER_CONTACT == False):
current_width = magnetic_calibration.get_current_gripper_width()
print(current_width)
fa.goto_gripper(current_width - gripper_step_size)
#magnetic_calibration.get_last_magnetic_data()
mag_data = magnetic_calibration.get_previous_magnetic_samples(
num_samples) # grab last ten samples
xyz_mag_data = mag_data[:, 6]
print(xyz_mag_data)
#slope = np.diff(xyz_mag_data, axis=0)
#print(slope)
# asign = np.sign(slope)
# signchange = ((np.roll(asign, 1) - asign) != 0).astype(int)
# print(signchange)
# first make sure the signal changes are large enough to not just be noise jitter
# then see if there is a signal change in slope - this signifies a contact.
#if(np.any(abs(np.diff(mag_data)>noise_level))):
#if(abs(np.sum(np.sign(np.diff(mag_data)))<numSamples) & abs(np.diff(mag_data))>noise_level):
if global_max is None:
global_max = np.max(xyz_mag_data, axis=0)
elif global_max < np.max(xyz_mag_data, axis=0):
global_max = np.max(xyz_mag_data, axis=0)
if global_max - xyz_mag_data[-1] > noise_level:
GRIPPER_CONTACT = True
min_gripper_width = current_width
fa.goto_gripper(min_gripper_width)
#min_contact = xyz_mag_data[-1]
print(min_gripper_width)
print("Global Max: ")
print(global_max)
gripper_step_size -= 0.0001
# if (np.any(np.logical_and((abs(slope)>noise_level), (slope > 0)))):
# # if last value is negative, contact. if last value is positive, release.
# # save max value so we can compare current value and strength of grip
# GRIPPER_CONTACT = True
# min_contact = np.min(xyz_mag_data, axis=0)
# min_gripper_width = current_width-gripper_step_size
# print(min_gripper_width)
# print("Min Magnetic Values: ")
# print(min_contact)
#input("Press enter to continue to next step")
#magnetic_calibration.close_gripper_magnetic_feedback()
# current_width = min_gripper_width
# num_samples = 100
# while(current_force_estimate < force_threshold):
# current_width -= gripper_step_size
# print(current_width)
# fa.goto_gripper(current_width)
# mag_data = magnetic_calibration.get_previous_magnetic_samples(num_samples) # grab last ten samples
# z_mag_data = np.max(mag_data[:,6])
# print(z_mag_data)
# current_force_estimate = global_min / z_mag_data
# print(current_force_estimate)
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_z)
for i in range(3):
fa.goto_pose_with_cartesian_control(beaker_position1)
fa.goto_pose_with_cartesian_control(beaker_position2)
fa.goto_gripper(0.0, force=20)
fa.goto_gripper(min_gripper_width)
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_z)
if i == 0:
force_value = 15
fa.goto_pose_with_cartesian_control(test_tube_1)
elif i == 1:
force_value = 30
fa.goto_pose_with_cartesian_control(test_tube_2)
elif i == 2:
force_value = 50
fa.goto_pose_with_cartesian_control(test_tube_3)
force_gripper_width = min_gripper_width
for j in range(5):
mag_data = magnetic_calibration.get_previous_magnetic_samples(
num_samples)
max_data = np.max(mag_data[:, 6], axis=0)
current_force_estimate = max_data - force_value
print("Force Threshold:")
print(current_force_estimate)
force_achieved = False
gripper_step_size = 0.0003
num_samples = 30
while (force_achieved == False):
force_gripper_width -= gripper_step_size
fa.goto_gripper(force_gripper_width)
#magnetic_calibration.get_last_magnetic_data()
mag_data = magnetic_calibration.get_previous_magnetic_samples(
num_samples) # grab last ten samples
print(np.mean(mag_data[:, 6]))
#slope = np.diff(xyz_mag_data, axis=0)
#print(slope)
# asign = np.sign(slope)
# signchange = ((np.roll(asign, 1) - asign) != 0).astype(int)
# print(signchange)
# first make sure the signal changes are large enough to not just be noise jitter
# then see if there is a signal change in slope - this signifies a contact.
#if(np.any(abs(np.diff(mag_data)>noise_level))):
#if(abs(np.sum(np.sign(np.diff(mag_data)))<numSamples) & abs(np.diff(mag_data))>noise_level):
if np.mean(mag_data[:, 6]) < current_force_estimate:
force_achieved = True
print(force_gripper_width)
if (j < 4):
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_y)
fa.goto_gripper(min_gripper_width)
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_z)
# for i in range(5):
# fa.goto_pose_with_cartesian_control(beaker_position1)
# fa.goto_pose_with_cartesian_control(beaker_position2)
# fa.goto_gripper(0.0, force = 20)
# fa.goto_gripper(min_gripper_width)
# fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_z)
# #fa.goto_gripper(min_gripper_width)
# fa.goto_pose_with_cartesian_control(test_tube_1)
# fa.goto_gripper(force_gripper_width)
# fa.goto_gripper(min_gripper_width)
fa.goto_pose_with_cartesian_control(beaker_position1)
fa.goto_pose_with_cartesian_control(beaker_position2)
fa.goto_gripper(0.0, force=20)
fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_z)
fa.goto_gripper(min_gripper_width)
fa.goto_pose_with_cartesian_control(initial_magnet_position3)
fa.goto_pose_with_cartesian_control(initial_magnet_position1)
fa.goto_pose_with_cartesian_control(initial_magnet_position2)
fa.open_gripper()
fa.goto_pose_with_cartesian_control(initial_magnet_position1)
# for i in range(3):
# for j in range(i+1):
# fa.goto_gripper(0.0, force = 20)
# fa.goto_gripper(min_gripper_width)
# if i < 2:
# fa.goto_pose_delta_with_cartesian_control(relative_pos_dist_x)
# fa.goto_pose_with_cartesian_control(current_position,5)
# current_joints = fa.get_joints()
# current_joints[6] += math.pi
# fa.goto_joints(list(current_joints))
# fa.goto_pose_delta_with_cartesian_control(relative_neg_dist_z)
# fa.open_gripper()
# fa.goto_pose_with_cartesian_control(initial_magnet_position1)
#fa.goto_pose_delta_with_cartesian_control(relative_neg_dist_z)
magnetic_calibration.stop_recording_data()
#fa.open_gripper()
print(magnetic_calibration.magnetic_data[0, :])
if args.filename is not None:
magnetic_calibration.saveData(args.filename, global_max,
min_gripper_width)
magnetic_calibration.plotData(magnetic_calibration.magnetic_data,
'Raw Data Over Time')
magnetic_calibration.plotGripperData(
magnetic_calibration.gripper_state_data, 'Raw Data Over Time')
magnetic_calibration.plotGripperData(magnetic_calibration.robot_state_data,
'Raw Data Over Time')
def run_main():
parser = argparse.ArgumentParser()
parser.add_argument('--filename', type=str, default=None)
args = parser.parse_args()
fa = FrankaArm(async_cmds=True)
fa.close_gripper()
while fa.is_skill_done() == False:
time.sleep(1)
# fa.reset_pose()
# fa.reset_joints()
drawer_2_z_height = 0.12919677
drawer_3_z_height = 0.08264024
drawer_height = drawer_3_z_height
initial_magnet_position1 = RigidTransform(
rotation=np.array([[0, -1, 0], [0, 0, 1], [-1, 0, 0]]),
translation=np.array([0.40926815, 0.20738414, drawer_height]),
from_frame='franka_tool',
to_frame='world')
initial_magnet_position2 = RigidTransform(
rotation=np.array([[0, -1, 0], [0, 0, 1], [-1, 0, 0]]),
translation=np.array([0.40926815, 0.23738414, drawer_height]),
from_frame='franka_tool',
to_frame='world')
relative_pos_dist_z = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.0,
0.08]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_pos_dist_y = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.08,
0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_rotation_z = RigidTransform(rotation=np.array([[0, 1, 0],
[-1, 0, 0],
[0, 0, 1]]),
translation=np.array([0.0, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_rotation_z = RigidTransform(rotation=np.array([[0, -1, 0],
[1, 0, 0],
[0, 0, 1]]),
translation=np.array(
[0.0, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_y = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([0.0, -0.13, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_z = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([0.0, 0.0, -0.08]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_pos_dist_x = RigidTransform(rotation=np.array([[1, 0,
0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.03, 0.0,
0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
relative_neg_dist_x = RigidTransform(
rotation=np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
translation=np.array([-0.03, 0.0, 0.0]),
from_frame='franka_tool',
to_frame='franka_tool')
fa.goto_pose_with_cartesian_control(initial_magnet_position1)
while fa.is_skill_done() == False:
time.sleep(1)
fa.goto_pose_with_cartesian_control(initial_magnet_position2)
while fa.is_skill_done() == False:
time.sleep(1)
magnetic_calibration = MagneticCalibration()
filename = '../calibration/2020-01-14 11-08 E1 2X Board Calibration.npz'
magnetic_calibration.load_calibration_file(filename)
num_samples = 10
noise_level = 1 # uT
#fa.goto_gripper(0.0, force=160)
# while fa.is_skill_done() == False:
# time.sleep(1)
magnetic_calibration.start_recording_data()
time.sleep(0.5)
greater_than_noise = False
fa.goto_pose_delta_with_cartesian_control(
relative_pos_dist_y, 5, stop_on_contact_forces=[20, 2, 20, 20, 20, 20])
while fa.is_skill_done() == False:
mag_data = magnetic_calibration.get_previous_magnetic_samples(
num_samples)
dif_mag_data = np.diff(mag_data[:, 5])
mean_change = np.mean(dif_mag_data)
print(mean_change)
if (abs(mean_change) > noise_level) and not greater_than_noise:
greater_than_noise = True
if greater_than_noise and abs(mean_change) < 1:
print("stopped skill")
fa.stop_skill()
time.sleep(0.01)
# while fa.is_skill_done() == False:
# time.sleep(1)
fa.goto_pose_delta_with_cartesian_control(relative_neg_dist_y, 5)
while fa.is_skill_done() == False:
time.sleep(1)
magnetic_calibration.stop_recording_data()
#fa.open_gripper()
while fa.is_skill_done() == False:
time.sleep(1)
print(magnetic_calibration.magnetic_data[0, :])
if args.filename is not None:
magnetic_calibration.saveData(args.filename)
magnetic_calibration.plotDataVsRobotState(
magnetic_calibration.magnetic_data,
magnetic_calibration.robot_state_data, 'Raw Data Over Distance')
magnetic_calibration.plotData(magnetic_calibration.magnetic_data,
'Raw Data Over Time')
magnetic_calibration.plotForceData(magnetic_calibration.force_state_data,
'Raw Data Over Time')
magnetic_calibration.plotRobotStateData(
magnetic_calibration.robot_state_data, 'Raw Data Over Time')
from frankapy import FrankaArm
if __name__ == '__main__':
print('Starting robot')
fa = FrankaArm()
fa.reset_joints()
pose = fa.get_pose()
pose.translation[0] = 0.75
# This should trigger an error
fa.goto_pose(pose)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--intrinsics_file_path',
type=str,
default=AZURE_KINECT_CALIB_DATA)
parser.add_argument('--transform_file_path',
type=str,
default=AZURE_KINECT_EXTRINSICS)
parser.add_argument('--filename', type=str, default=None)
args = parser.parse_args()
# rospy.init_node("Test azure kinect calibration")
print('Starting robot')
fa = FrankaArm()
cv_bridge = CvBridge()
ir_intrinsics = CameraIntrinsics.load(args.intrinsics_file_path)
kinect2_overhead_to_world_transform = RigidTransform.load(
args.transform_file_path)
print('Opening Grippers')
fa.open_gripper()
print('Reset with pose')
fa.reset_pose(10)
print('Reset with joints')
fa.reset_joints()
import argparse
from frankapy import FrankaArm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--time', '-t', type=float, default=100)
parser.add_argument('--open_gripper', '-o', action='store_true')
args = parser.parse_args()
print('Starting robot')
fa = FrankaArm()
if args.open_gripper:
fa.open_gripper()
fa.selective_guidance_mode(
args.time,
use_impedance=True,
use_ee_frame=True,
cartesian_impedances=[600.0, 0.0, 0.0, 0.0, 0.0, 0.0])
from frankapy import FrankaArm
import numpy as np
from autolab_core import RigidTransform
if __name__ == '__main__':
print('Starting robot')
fa = FrankaArm()
xtranslation_3cm = RigidTransform(rotation=np.array([[1, 0, 0], [0, 1, 0],
[0, 0, 1]]),
translation=np.array([0.03, 0, 0]),
from_frame='franka_tool',
to_frame='world')
random_position = RigidTransform(
rotation=np.array([[0.9323473, -0.35858258, 0.04612846],
[-0.35996283, -0.93259467, 0.02597504],
[0.03370496, -0.04082229, -0.99859775]]),
translation=np.array([0.39247965, -0.21613652, 0.3882055]),
from_frame='franka_tool',
to_frame='world')
print(fa.get_pose().translation)
print(fa.get_joints())
desired_joints_1 = [
-0.52733715, 0.25603565, 0.47721503, -1.26705864, 0.00600359,
1.60788199, 0.63019184
]
import numpy as np
from autolab_core import RigidTransform
from frankapy import FrankaArm
if __name__ == "__main__":
fa = FrankaArm()
# reset franka to its home joints
fa.reset_joints()
# read functions
T_ee_world = fa.get_pose()
print('Translation: {} | Rotation: {}'.format(T_ee_world.translation, T_ee_world.quaternion))
joints = fa.get_joints()
print('Joints: {}'.format(joints))
gripper_width = fa.get_gripper_width()
print('Gripper width: {}'.format(gripper_width))
# gripper controls
print('Closing gripper')
fa.close_gripper()
print('Opening gripper to a specified position')
fa.goto_gripper(0.02)
print('Opening gripper all the way')
fa.open_gripper()
from frankapy import FrankaArm
import numpy as np
from autolab_core import RigidTransform
fa = FrankaArm()
trans, rot = np.load("above_pose.npy")
#fa.close_gripper()
above_position = RigidTransform(rotation=rot,
translation=trans,
from_frame='franka_tool',
to_frame='world')
fa.goto_pose_with_cartesian_control(
above_position, cartesian_impedances=[3000, 3000, 100, 300, 300, 300])
fa.open_gripper()
input("Add gripper")
fa.close_gripper()
trans, rot = np.load("insert_pose.npy")
insert_position = RigidTransform(rotation=rot,
translation=trans,
from_frame='franka_tool',
to_frame='world')
insert_position.translation[-1] -= 0.01 # manual adjustment
fa.goto_pose_with_cartesian_control(
insert_position, cartesian_impedances=[20, 20, 2000, 300, 300, 300]
) #one of these but with impedance control? compliance comes from the matrix so I think that's good enough
magnetic_data = calibrated[4:7]-transform_ref[0:3]
return magnetic_data
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--intrinsics_file_path', type=str, default=AZURE_KINECT_CALIB_DATA)
parser.add_argument('--transform_file_path', type=str, default=AZURE_KINECT_EXTRINSICS)
parser.add_argument('--object', type=str, default=AZURE_KINECT_EXTRINSICS)
args = parser.parse_args()
# rospy.init_node("Test azure kinect calibration")
print('Starting robot')
fa = FrankaArm()
cv_bridge = CvBridge()
ir_intrinsics = CameraIntrinsics.load(args.intrinsics_file_path)
kinect2_overhead_to_world_transform = RigidTransform.load(args.transform_file_path)
print('Opening Grippers')
fa.open_gripper()
print('Reset with pose')
fa.reset_pose()
print('Reset with joints')
fa.reset_joints()
import numpy as np
from autolab_core import RigidTransform
from frankapy import FrankaArm, SensorDataMessageType
from frankapy import FrankaConstants as FC
from frankapy.proto_utils import sensor_proto2ros_msg, make_sensor_group_msg
from frankapy.proto import PosePositionSensorMessage, ShouldTerminateSensorMessage, CartesianImpedanceSensorMessage
from franka_interface_msgs.msg import SensorDataGroup
from frankapy.utils import min_jerk, min_jerk_weight
import rospy
if __name__ == "__main__":
fa = FrankaArm()
fa.reset_joints()
rospy.loginfo('Generating Trajectory')
p0 = fa.get_pose()
p1 = p0.copy()
T_delta = RigidTransform(translation=np.array([0, 0, 0.2]),
rotation=RigidTransform.z_axis_rotation(
np.deg2rad(30)),
from_frame=p1.from_frame,
to_frame=p1.from_frame)
p1 = p1 * T_delta
fa.goto_pose(p1)
T = 5
dt = 0.02
ts = np.arange(0, T, dt)
'/path/to/azure_kinect_calibration/calib/azure_kinect_left/kinect2_left_to_world.tf'
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--intrinsics_file_path',
type=str,
default=AZURE_KINECT_CALIB_DATA)
parser.add_argument('--transform_file_path',
type=str,
default=AZURE_KINECT_EXTRINSICS)
parser.add_argument('--filename', type=str, default=None)
args = parser.parse_args()
# rospy.init_node("Test azure kinect calibration")
print('Starting robot')
fa = FrankaArm()
cv_bridge = CvBridge()
ir_intrinsics = CameraIntrinsics.load(args.intrinsics_file_path)
kinect2_left_to_world_transform = RigidTransform.load(
args.transform_file_path)
# print('Opening Grippers')
# fa.open_gripper()
print('Reset with pose')
fa.reset_pose()
print('Reset with joints')
fa.reset_joints()
import numpy as np
from frankapy import FrankaArm, SensorDataMessageType
from frankapy import FrankaConstants as FC
from frankapy.proto_utils import sensor_proto2ros_msg, make_sensor_group_msg
from frankapy.proto import ForcePositionSensorMessage, ForcePositionControllerSensorMessage
from franka_interface_msgs.msg import SensorDataGroup
from frankapy.utils import transform_to_list, min_jerk
from tqdm import trange
import rospy
if __name__ == "__main__":
fa = FrankaArm()
fa.reset_joints()
fa.close_gripper()
while True:
input(
'Presse [Enter] to enter guide mode and move robot to be on top of a flat surface.'
)
fa.run_guide_mode()
while True:
inp = input('[r]etry or [c]ontinue? ')
if inp not in ('r', 'c'):
print('Please give valid input!')
else:
break
if inp == 'c':
break
import GPy as gpy
from autolab_core import RigidTransform, YamlConfig
from visualization import Visualizer3D as vis3d
from perception_utils.apriltags import AprilTagDetector
from perception_utils.realsense import get_first_realsense_sensor
from modelfree import processimgs, vae
from modelfree.ILPolicy import ILPolicy, process_action_data
#from test_module.test_behaviour_cloning import test_behaviour_cloning as get_il_policy
from perception import Kinect2SensorFactory, KinectSensorBridged
from sensor_msgs.msg import Image
from perception.camera_intrinsics import CameraIntrinsics
robot = True
if robot:
from frankapy import FrankaArm
fa = FrankaArm()
import ipdb
ipdb.set_trace()
else:
rospy.init_node("fo")
class Robot():
def __init__(self, visualize=False, setup_pb=True):
self.contact_threshold = -3.5 # less than is in contact
self.autoencoder = None
self.il_policy = None
self.bridge = cv_bridge.CvBridge()
self.shape_to_goal_loc = {}
self.shape_type_to_ids = {Rectangle: (0, 1, 2, 3)}
self.shape_goal_type_to_ids = {Rectangle: 1}
import argparse
from frankapy import FrankaArm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--use_pose', '-u', action='store_true')
parser.add_argument('--close_gripers', '-c', action='store_true')
args = parser.parse_args()
print('Starting robot')
fa = FrankaArm()
if args.use_pose:
print('Reset with pose')
fa.reset_pose()
else:
print('Reset with joints')
fa.reset_joints()
if args.close_gripers:
print('Closing Grippers')
fa.close_gripper()
else:
print('Opening Grippers')
fa.open_gripper()
def wait_for_enter():
if sys.version_info[0] < 3:
raw_input('Press Enter to continue:')
else:
input('Press Enter to continue:')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--time', '-t', type=float, default=10)
parser.add_argument('--open_gripper', '-o', action='store_true')
args = parser.parse_args()
print('Starting robot')
fa = FrankaArm()
if args.open_gripper:
fa.open_gripper()
print(
'Be very careful!! Make sure the robot can safely move to HOME JOINTS Position.'
)
wait_for_enter()
fa.reset_joints()
print('Using default joint impedances to move back and forth.')
wait_for_enter()
fa.goto_joints(FC.READY_JOINTS,
joint_impedances=FC.DEFAULT_JOINT_IMPEDANCES)
fa.goto_joints(FC.HOME_JOINTS)
print('Now using different joint impedances to move back and forth.')
translation=np.array([0.52567046, 0.28332678, 0.37527456]),
from_frame='franka_tool', to_frame='world')
fa.goto_pose_with_cartesian_control(desired_tool_pose,
duration=duration,
cartesian_impedances=gains)
if open_gripper:
fa.open_gripper()
if __name__ == '__main__':
# Examples and for collecting robot poses
# Everything is hard coded :/
print('Starting robot')
fa = FrankaArm()
go2start(fa)
dw = DoorWorld()
print(fa.get_pose())
print(fa.get_joints())
input("Press enter to continue")
go2door(fa, close_gripper=True, use_planner = True, world=dw)
fa.close_gripper()
res = turn_knob(fa, use_planner = True, world = dw)
if res == "expected_model_failure" or res == "model_deviation":
gains1 = [1000,1000,1000,1,1,1]
dmp_info2 = "/home/stevenl3/misc_backups/robot_data_door_turn2_position_weights.pkl"
fa.execute_position_dmp(dmp_info2, duration=10,
skill_desc='position_dmp', cartesian_impedance=gains1)
import numpy as np
import math
import rospy
import argparse
import pickle
from autolab_core import RigidTransform, Point
from frankapy import FrankaArm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--pose_dmp_weights_file_path', '-f', type=str)
args = parser.parse_args()
print('Starting robot')
fa = FrankaArm()
pose_dmp_file = open(args.pose_dmp_weights_file_path, "rb")
pose_dmp_info = pickle.load(pose_dmp_file)
fa.execute_pose_dmp(pose_dmp_info, duration=4.2)
import argparse
from frankapy import FrankaArm
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--time', '-t', type=float, default=100)
parser.add_argument('--open_gripper', '-o', action='store_true')
args = parser.parse_args()
print('Starting robot')
fa = FrankaArm()
if args.open_gripper:
fa.open_gripper()
print('Applying 0 force torque control for {}s'.format(args.time))
fa.apply_effector_forces_torques(args.time, 0, 0, 0)
