def __init__(self, randomize=False, rnn_xml=None, verbose=False):
"""Initialize
Creates the environment and viewer objects required to run the neural
network PA10 robotic arm simulation.
Arguments:
randomize: Determines if the test article gates will be randomized.
(Default: False)
rnn_xml: A XML filename containing neural network parameters. If
None, a new neural network will be trained until convergence.
(Default: None)
verbose: Determines the level out debug output generated.
(Default: False)
"""
# Generate the XODE file
XODE_FILENAME = 'model' # .xode is appended automatically
print('>>> Generating world model')
if os.path.exists('./' + XODE_FILENAME + '.xode'):
os.remove('./' + XODE_FILENAME + '.xode')
xode_model = NeuralSimWorld(name=XODE_FILENAME,
randomize_test_article=randomize)
xode_model.generate()
print('>>> Generating RNN')
# Determine if we need to train the neural network
if rnn_xml is not None:
print('>>> Loading RNN from file')
self.rnn = network.PathPlanningNetwork()
self.rnn.load_network_from_file(rnn_xml)
else:
print('>>> Training new RNN')
self.rnn = network.train_path_planning_network()
self.rnn.save_network_to_file(constants.G_RNN_XML_OUT)
print('>>> Starting kinematics engine')
self.kinematics = PA10Kinematics()
# Start environment
print('>>> Starting environment')
self.env = EnvironmentInterface(
xode_filename='./' + XODE_FILENAME + '.xode',
realtime=False,
verbose=verbose,
gravity=constants.G_ENVIRONMENT_GRAVITY)
# Start viewer
print('>>> Starting viewer')
self.viewer = ViewerInterface(verbose=verbose)
self.viewer.start()
# Set up all grouped bodies in the environment
self.env.groups = {
'pointer': ['tooltip', 'stick'],
}
return
def __init__(self, randomize=False, network=False, verbose=False):
"""Initialize
Creates the environment, viewer, and (Phantom Omni) controller objects
required to run the human data capture simulation.
Arguments:
randomize: Determines if the test article gates will be randomized.
(Default: False)
network: Determines if the omni connection is local or networked.
(Default: False)
verbose: Determines the level out debug output generated.
(Default: False)
"""
# Generate the XODE file
XODE_FILENAME = 'model' # .xode is appended automatically
print('>>> Generating world model')
if os.path.exists('./'+XODE_FILENAME+'.xode'):
os.remove('./'+XODE_FILENAME+'.xode')
xode_model = TrainingSimWorld(
name=XODE_FILENAME,
randomize_test_article=randomize
)
xode_model.generate()
# Start environment
print('>>> Starting environment')
self.env = EnvironmentInterface(
xode_filename='./'+XODE_FILENAME+'.xode',
realtime=False,
verbose=verbose,
gravity=constants.G_ENVIRONMENT_GRAVITY
)
# Set up all grouped bodies in the environment
self.env.groups = {
'pointer': ['tooltip', 'stick'],
}
# Start viewer
print('>>> Starting viewer')
self.viewer = ViewerInterface(verbose=verbose)
self.viewer.start()
# Start controller
print('>>> Starting Phantom Omni interface')
self.omni = PhantomOmniInterface()
if network:
ip = raw_input('<<< Enter host ip: ')
port = int(raw_input('<<< Enter tcp port: '))
else:
ip = constants.G_IP_LOCAL_DEFAULT
port = constants.G_PORT_DEFAULT
# Try to connect to the Phantom Omni controller
self.omni.connect(ip, port)
self.saved_data = np.array([])
return
