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()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
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)
temp = np.random.randint(1, len(a))
position = a[temp][:-4][1:-1].split(",")
for i in range(len(position)):
position[i] = float(position[i])
return position, np.load("/home/ljt/Desktop/data/"+a[temp])
def file_name(file_dir="/home/ljt/Desktop/ws/src/fetch_moveit_config/data/memory"):
for root, dirs, files in os.walk(file_dir):
for i in range(len(files)):
files[i] = int(files[i][:-4])
return max(files)
if __name__ == "__main__":
robot = Robot()
s_dim = robot.state_dim
a_dim = robot.action_dim
a_bound = robot.action_bound
cubm = CubesManager()
rl = DQN()
print "----loading previous model----"
rl.eval_net = torch.load('eval_dqn.pkl').cuda()
rl.target_net = torch.load('target_dqn.pkl').cuda()
robot.reset()
start_position = robot.gripper.get_current_pose("gripper_link").pose.position # 初始的夹爪位置
begin = time.clock()
# print "loading memory!!!"
# rl.memory = np.load("/home/ljt/Desktop/ws/src/fetch_moveit_config/data/memory/"+str(file_name())+".npy")
for i in range(1, MAX_EPISODES):
import cv2
from DQN import DQN
from Env import Robot, CubesManager
MAX_EPISODES = 5000
MAX_EP_STEPS = 200
MEMORY_CAPACITY = 50
if __name__ == "__main__":
robot = Robot()
s_dim = robot.state_dim
a_dim = robot.action_dim
a_bound = robot.action_bound
cubm = CubesManager()
rl = DQN()
for i in range(MAX_EPISODES):
robot.reset()
cubm.reset_cube(rand=True)
Box_position = cubm.read_cube_pose("cube1")
Box_position[0] -= 0.2
Box_position[2] -= 0.1
robot.Box_position = Box_position
# print(cubm.read_cube_pose("cube1"))
# print(robot.Box_position)
s = robot.get_state()
st = 0
rw = 0
while True:
st += 1