def collect_data():
robot = Robot()
cubusm = CubesManager()
for i in range(MAX_PICTURE_NUM):
cubusm.reset_cube(rand=True)
Box_position = cubusm.read_cube_pose("demo_cube")
# print "cube position:", str(Box_position)
joint, view = robot.get_state()
rgb, dep = robot.get_rgb_dep()
# b, g, r = cv2.split(rgb)
# print view[0,0,0]
# print dep
# rgb = cv2.merge([r, g, b])
# print dep
# plt.imshow(dep)
# plt.show()
rgb = cv2.resize(rgb, (224, 224))
dep = cv2.resize(dep, (224, 224))
# print dep
cv2.imwrite(
"/home/ljt/Desktop/images/rgb/" + str(Box_position) + ".png", rgb)
# cv2.imwrite("/home/ljt/Desktop/ws/src/fetch_moveit_config/images/dep/" + str(Box_position) + ".png", dep)
# a = np.array(rgb).shape
# print a
# print "camera image shape:", view.shape
np.save("/home/ljt/Desktop/images/dep/" + str(Box_position), dep)
import math
from camera import RGBD
from MofanDDPG import DDPG
from Env import Robot, CubesManager
import copy
import numpy as np
MAX_EPISODES = 900
MAX_EP_STEPS = 5
ON_TRAIN = True
if __name__ == '__main__':
# set env
robot = Robot()
cubm = CubesManager()
observation_dim = 3
action_dim = 3
action_bound = -1, 1
# set RL method (continuous)
rl = DDPG(action_dim, observation_dim, action_bound)
number = 0
steps = []
# start training
for i in range(MAX_EPISODES):
cubm.reset_cube(rand=True)
Box_position = cubm.read_cube_pose("demo_cube")
print "cube position:", Box_position
robot.Box_position = copy.deepcopy(Box_position)
now_position = robot.gripper.get_current_pose(
import math from camera import RGBD from DQN import DQN from Env import Robot, CubesManager import copy import rospy robot = Robot() cum = CubesManager() cum.reset_cube(False) robot.test1() robot.reset()
import torch
from camera import RGBD
from DDPG import DDPG
from DQN import DQN
from Env import Robot, CubesManager
import copy
import rospy
MAX_EPISODES = 5000
MAX_EP_STEPS = 100
MEMORY_CAPACITY = 1000
if __name__ == "__main__":
robot = Robot()
s_dim = robot.state_dim
a_dim = robot.action_dim
a_bound = robot.action_bound
cubm = CubesManager()
rl = DQN()
rl.eval_net = torch.load('eval_dqn.pkl')
rl.target_net = torch.load('target_dqn.pkl')
robot.reset()
start_position = robot.gripper.get_current_pose(
"gripper_link").pose.position # 初始的夹爪位置
st = 0
rw = 0
for i in range(1, MAX_EPISODES):
cubm.reset_cube(rand=True)
Box_position = cubm.read_cube_pose("cube1") # 获取物块位置
# print "cube position:", Box_position
robot.Box_position = copy.deepcopy(Box_position)
"""
goal = control_msgs.msg.GripperCommandGoal()
goal.command.position = OPENED_POS
self._client.send_goal_and_wait(goal, rospy.Duration(10))
def close(self, width=0.0, max_effort=MAX_EFFORT):
"""Closes the gripper.
Args:
width: The target gripper width, in meters. (Might need to tune to
make sure the gripper won't damage itself or whatever it's
gripping.)
max_effort: The maximum effort, in Newtons, to use. Note that this
should not be less than 35N, or else the gripper may not close.
"""
assert CLOSED_POS <= width <= OPENED_POS
goal = control_msgs.msg.GripperCommandGoal()
goal.command.position = width
goal.command.max_effort = max_effort
self._client.send_goal_and_wait(goal, rospy.Duration(10))
if __name__ == "__main__":
test = CubesManager()
i = 1
while i < 10:
print(i)
test.reset_cube(rand=True)
rospy.sleep(1)
i += 1