def data_acquisition(self,num_of_data_set,rep_data):
print 'Training Started'
features=[]
labels=[]
for i in range(num_of_data_set):
action = np.random.rand(1, 3)
action[0, 0] = (2 * action[0, 0] - 1.0) * 1.0
action[0, 1] = (2 * action[0, 1] - 1.0) * 0.5
action[0, 2] = (2 * action[0, 2] - 1.0) * 0.25
print 'Training for dataset', (i+1)
real_robot_action = rospy.ServiceProxy('real_robot', RobotAction)
req = RobotActionRequest()
req.reset = True
resp =real_robot_action(req)
for j in range(rep_data):
#print 'Rep set',(j+1)
f=np.append(np.array(resp.robot_state),action)
features.append(f)
req = RobotActionRequest()
req.reset = False
req.action = action.reshape((3))
resp = real_robot_action(req)
labels.append(resp.robot_state)
features=np.asarray(features)
labels=np.asarray(labels)
print features
print "Acquisition Completed"
return features,labels
def get_data(n):
# get data set
real_robot_action = rospy.ServiceProxy('real_robot', RobotAction)
feature = []
label = []
#print(resp_real)
#print('------')
for k in range(n):
req = RobotActionRequest()
req.reset = True
resp_real = real_robot_action(req)
action = numpy.random.rand(1, 3)
action[0, 0] = (2 * action[0, 0] - 1.0) * 1.0
action[0, 1] = (2 * action[0, 1] - 1.0) * 0.5
action[0, 2] = (2 * action[0, 2] - 1.0) * 0.25
robsta = resp_real.robot_state
robsta = numpy.array(robsta)
sfeature = [
robsta[0], robsta[1], robsta[2], robsta[3], robsta[4], robsta[5],
action[0, 0], action[0, 1], action[0, 2]
]
for i in range(200):
#print(sfeature)
req = RobotActionRequest()
req.reset = False
req.action = action.reshape((3))
resp_real = real_robot_action(req)
#print(resp_real.robot_state)
#print(action)
feature.append(sfeature)
print(resp_real)
label.append(numpy.array(resp_real.robot_state))
#print(label)
#rospy.spin()
return feature, label
def get_new_state(self, action, k):
req = RobotActionRequest()
req.reset = True
resp = self.robot_new_state(req)
for i in range(self.num_steps):
req = RobotActionRequest()
req.reset = False
req.action = action.reshape((3))
resp = self.robot_new_state(req)
self.train_data_matrix[k, i, 6:9] = action.reshape((3))
self.train_data_matrix[k, i, 9:15] = resp.robot_state
time.sleep(0.01)
self.train_data_matrix[k, 1:self.num_steps,
0:6] = self.train_data_matrix[k,
0:self.num_steps -
1, 9:15]
def single_test(self, action):
req = RobotActionRequest()
req.reset = True
# send request to reset
resp = self.get_action(req)
for i in range(self.num_steps):
req = RobotActionRequest()
req.reset = False
req.action = action
# get training data
self.features.append((np.append(resp.robot_state,
req.action)).tolist())
# send request to move real_robot
resp = self.get_action(req)
self.labels.append(resp.robot_state)
def single_test(self, action):
req = RobotActionRequest()
req.reset = True
resp = self.real_robot_action(req)
resp = self.fake_robot_action(req)
for i in range(self.num_steps):
req = RobotActionRequest()
req.reset = False
req.action = action.reshape((3))
resp_real = self.real_robot_action(req)
message = RobotState()
message.robot_name = str('real_robot')
message.robot_state = resp_real.robot_state
self.pub.publish(message)
resp_fake = self.fake_robot_action(req)
message = RobotState()
message.robot_name = str('fake_robot')
message.robot_state = resp_fake.robot_state
self.pub.publish(message)
#time.sleep(0.01)
return self.score_state(resp_real.robot_state, resp_fake.robot_state)
def perturb(self, action):
req_real = RobotActionRequest()
req_real.reset = True
# send request to reset real_robot config
resp_real = self.real_robot_action(req_real)
collect_interval = 1 # how many steps we skip
# apply a constant action
for j in range(self.perturb_steps):
# create a new request
req_real = RobotActionRequest()
req_real.reset = False
req_real.action = action
is_collected = j % collect_interval == 0
if is_collected:
# collect feature
self.features.append(
np.append(resp_real.robot_state, req_real.action).tolist())
# send request to move real_robot
resp_real = self.real_robot_action(req_real)
if is_collected:
# collect label
self.labels.append(resp_real.robot_state)
def training_data(self): #get the data to train fake robot
n = 0
for k in range(0, self.num_random_tests):
action = np.random.rand(1, 3)
action[0, 0] = (2 * action[0, 0] - 1.0) * 1.0
action[0, 1] = (2 * action[0, 1] - 1.0) * 0.5
action[0, 2] = (2 * action[0, 2] - 1.0) * 0.25
req_real = RobotActionRequest()
req_real.reset = True
resp = self.real_robot_action(req_real)
self.data_fea[k * self.num_steps, 0:6] = np.array(resp.robot_state)
print(k)
for i in range(self.num_steps):
req_real = RobotActionRequest()
req_real.reset = False
req_real.action = action.reshape((3))
resp_real = self.real_robot_action(req_real)
self.data_fea[n, 6:9] = req_real.action
self.label[n] = resp_real.robot_state
n += 1
self.data_fea[n, 0:6] = resp_real.robot_state
print('trainning finished')
self.data_fea = np.delete(self.data_fea, -1, axis=0)