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
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
class TrainingSimulation(object):
"""TrainingSimulation class
Responsible for initialization of all children objects such as the
Open Dynamics Engine environment, Phantom Omni controller, and OpenGL
viewer. Starts the main event loop with real-time constraints.
Attributes:
env: The Open Dynamics Engine environment.
omni: The Phantom Omni robotic controller connection.
viewer: The OpenGL viewer for the ODE environment.
saved_data: A list of tuples containing data from the simulation.
Methods:
start: Begins the main event loop.
"""
env = None
omni = None
viewer = None
saved_data = None
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
def start(self, fps):
"""Start
Begin the continuous event loop for the simulation. This event loop
can be exited using the ctrl+c keyboard interrupt. Real-time
constraints are enforced.
Arguments:
fps: The value of frames per second of the simulation.
"""
paused = False
stopped = False
# Define the simulation frame rate
t = 0.0
dt = 1.0 / float(fps) # [s]
# Keep track of time overshoot in the case that simulation time must be
# increased in order to maintain real-time constraints
t_overshoot = 0.0
while not stopped:
t_start = time.time()
# If the last calculation took too long, catch up
dt_warped = dt + t_overshoot
self.env.set_dt(dt_warped)
self.omni.set_dt(dt_warped)
# Determine if the viewer is stopped. Then we can quit
if self.viewer.is_dead:
break
if paused:
self.env.step(paused=True)
continue
# Populate the controller with the most up-to-date data
self.omni.update()
# Determine the input data to record
sample_input = np.array([
t,
]).flatten()
# Capture the gate position at each time step
for gate_idx in range(constants.G_NUM_GATES):
gate_pos = np.array(self.env.get_body_pos('gate%d'%gate_idx)).flatten()
gate_rot = constants.G_GATE_NORM_ROT[gate_idx]
sample_input = np.hstack((sample_input, gate_pos, gate_rot))
# Determine the output data to record
sample_output = np.array([
self.env.get_body_pos('tooltip'),
]).flatten()
# Join the sample input/output
data_sample = np.hstack((sample_input, sample_output))
# Save the data
self.save_data(data_sample)
# Get the updated linear/angular velocities of the tooltip
linear_vel = self.omni.get_linear_vel()
angular_vel = self.omni.get_angular_vel()
# Set the linear and angular velocities of the simulation
self.env.set_group_linear_vel('pointer', linear_vel)
self.env.set_group_angular_vel('pointer', angular_vel)
# Step through the world by 1 time frame
self.env.step()
t += dt_warped
# Determine the difference in virtual vs actual time
t_warped = dt - (time.time() - t_start)
# Attempt to enforce real-time constraints
if t_warped >= 0.0:
# The calculation took less time than the virtual time. Sleep
# the rest off
time.sleep(t_warped)
t_overshoot = 0.0
else:
# The calculation took more time than the virtual time. We need
# to catch up with the virtual time on the next time step
t_overshoot = -t_warped
return
def save_data(self, data_sample):
"""Save Data
Saves a sample of data into the simulation's saved data array.
Arguments:
data_sample: A sample of input/output data from a single time step.
"""
if len(self.saved_data):
self.saved_data = np.vstack((self.saved_data, data_sample))
else:
self.saved_data = data_sample.copy()
return
def __del__(self):
"""Delete (del)
Attempts to shut down any active engines and kills off spawned
class objects.
"""
# Kill the OpenGL viewer process
if self.viewer is not None:
self.viewer.stop()
del self.viewer
# Kill the ODE environment objects
if self.env is not None:
self.env.stop()
del self.env
# Kill the Phantom Omni controller process
if self.omni is not None:
self.omni.disconnect()
del self.omni
return
class NeuralSimulation(object):
"""NeuralSimulation class
Responsible for initialization of all children objects such as the
Open Dynamics Engine environment and OpenGL viewer. Starts the main
event loop with real-time constraints.
Attributes:
env: The Open Dynamics Engine environment.
viewer: The OpenGL viewer for the ODE environment.
Methods:
start: Begins the main event loop.
"""
env = None
viewer = None
rnn = None
kinematics = None
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 start(self, fps, fast_step=False):
"""Start
Begin the continuous event loop for the simulation. This event loop
can be exited using the ctrl+c keyboard interrupt. Real-time
constraints are enforced. [Hz]
Arguments:
fps: The value of frames per second of the simulation.
fast_step: If True, the ODE fast step algorithm will be used.
This is faster and requires less memory but is less accurate.
(Default: False)
"""
paused = False
stopped = False
# Define the total time for the tooltip traversal
t_total = 20.0
# Define the simulation frame rate
t = 0.0 # [s]
dt = 1.0 / float(fps) # [s]
# Keep track of time overshoot in the case that simulation time must be
# increased in order to maintain real-time constraints
t_overshoot = 0.0
# Get the initial path position (center of gate7)
pos_start = self.env.get_body_pos('gate7') # [m]
# Get the first position of the PA10 at rest
pos_init = self.env.get_body_pos('tooltip') # [m]
# Calculate the new required joint angles of the PA10
#pa10_joint_angles = self.kinematics.calc_inverse_kinematics(pos_init, pos_start)
# TODO: Move the PA10 end-effector to the starting position along the path
# TODO: TEMP - Move the temporary end-effector pointer to the starting position
self.env.set_group_pos('pointer', pos_start)
# Generate long-term path from initial position
t_input = np.linspace(start=0.0, stop=1.0, num=t_total / dt)
t_input = np.reshape(t_input, (len(t_input), 1))
rnn_path = self.rnn.extrapolate(t_input, [pos_start], len(t_input) - 1)
# Add the initial condition point back onto the data
rnn_path = np.vstack((pos_start, rnn_path))
# Retrieve one set of standard gate position/orientation data
file_path = pathutils.list_data_files(constants.G_TRAINING_DATA_DIR)[0]
gate_data = datastore.retrieve(file_path)
gate_start_idx = constants.G_GATE_IDX
gate_end_idx = gate_start_idx + constants.G_NUM_GATE_INPUTS
# Reshape the gate positions data
gate_data = gate_data[0:1, gate_start_idx:gate_end_idx]
gate_data = np.tile(gate_data, (len(rnn_path), 1))
# Complete the rnn path data
rnn_path = np.hstack((t_input, gate_data, rnn_path))
# Save generated path for later examination
datastore.store(rnn_path, constants.G_RNN_STATIC_PATH_OUT)
# Define a variable to hold the final path (with real-time correction)
final_path = rnn_path[:-1].copy()
path_saved = False
# Detect all path segments between gates in the generated path
segments = pathutils._detect_segments(rnn_path)
path_idx = 0
x_path_offset = np.array([0.0, 0.0, 0.0]) # [m]
v_curr = np.array([0.0, 0.0, 0.0]) # [m/s]
a_max = constants.G_MAX_ACCEL # [m/s^2]
# Get the static table position
x_table = self.env.get_body_pos('table')
while not stopped:
t_start = time.time()
# If the last calculation took too long, catch up
dt_warped = dt + t_overshoot
self.env.set_dt(dt_warped)
# Determine if the viewer is stopped. Then we can quit
if self.viewer.is_dead:
break
# Pause the simulation if we are at the end
if path_idx == len(rnn_path) - 1 or paused:
self.env.step(paused=True, fast=fast_step)
# If we have really hit the end of the simulation, save/plot the path
if not paused and not path_saved:
# Save the final data to a file
datastore.store(final_path,
constants.G_RNN_DYNAMIC_PATH_OUT)
path_saved = True
continue
# Not a very elegant solution to pausing at the start, but it works
if t <= 1000.0:
self.env.step(paused=True, fast=fast_step)
t += dt_warped
continue
# Determine the current path segment
curr_segment_idx = 0
for segment_idx, segment_end in enumerate(segments):
if path_idx <= segment_end:
curr_segment_idx = segment_idx
break
x_curr = pathutils.get_path_tooltip_pos(rnn_path,
path_idx) + x_path_offset
x_next = pathutils.get_path_tooltip_pos(
rnn_path, path_idx + 1) + x_path_offset
# Get the expected gate position
x_gate_expected = pathutils.get_path_gate_pos(
rnn_path, segments[curr_segment_idx], curr_segment_idx)
# Get the actual gate position
x_gate_actual = self.env.get_body_pos('gate%d' % curr_segment_idx)
# Calculate the new position from change to new gate position
dx_gate = x_gate_actual - (x_gate_expected + x_path_offset)
x_new = x_next + dx_gate
# Calculate the new velocity
v_new = (x_new - x_curr) / dt_warped
# Calculate the new acceleration
a_new = (v_new - v_curr) / dt_warped
# Calculate the acceleration vector norm
a_new_norm = np.linalg.norm(a_new)
# Limit the norm vector
a_new_norm_clipped = np.clip(a_new_norm, -a_max, a_max)
# Determine the ratio of the clipped norm, protect against divide by zero
if a_new_norm != 0.0:
ratio_unclipped = a_new_norm_clipped / a_new_norm
else:
ratio_unclipped = 0.0
# Scale the acceleration vector by this ratio
a_new = a_new * ratio_unclipped
# Calculate the new change in velocity
dv_new = a_new * dt_warped
v_new = v_curr + dv_new
# Calculate the new change in position
dx_new = v_new * dt_warped
x_new = x_curr + dx_new
# Modify final path data with current tooltip and gate positions
pathutils.set_path_time(final_path, path_idx, t)
pathutils.set_path_tooltip_pos(final_path, path_idx, x_curr)
for gate_idx in range(constants.G_NUM_GATES):
gate_name = 'gate%d' % gate_idx
x_gate = self.env.get_body_pos(gate_name)
pathutils.set_path_gate_pos(final_path, path_idx, gate_idx,
x_gate)
# Store this velocity for the next time step
v_curr = v_new
# Recalculate the current offset
x_path_offset += x_new - x_next
# Perform inverse kinematics to get joint angles
pa10_joint_angles = self.kinematics.calc_inverse_kinematics(
x_curr, x_new)
# TODO: TEMP - MOVE ONLY POINTER, NO PA10
self.env.set_group_pos('pointer', x_new)
if constants.G_TABLE_IS_OSCILLATING:
# Move the table with y-axis oscillation
x_table_next = shaker_table(t, x_table)
else:
x_table_next = x_table
self.env.set_body_pos('table', x_table_next)
# Step through the world by 1 time frame and actuate pa10 joints
self.env.performAction(pa10_joint_angles, fast=fast_step)
# Update current time after this step
t += dt_warped
path_idx += 1
# Determine the difference in virtual vs actual time
t_warped = dt - (time.time() - t_start)
# Attempt to enforce real-time constraints
if t_warped >= 0.0:
# The calculation took less time than the virtual time. Sleep
# the rest off
time.sleep(t_warped)
t_overshoot = 0.0
else:
# The calculation took more time than the virtual time. We need
# to catch up with the virtual time on the next time step
t_overshoot = -t_warped
return
def __del__(self):
"""Delete (del)
Attempts to shut down any active engines and kills off spawned
class objects.
"""
# Kill the OpenGL viewer process
if self.viewer is not None:
self.viewer.stop()
del self.viewer
# Kill the ODE environment objects
if self.env is not None:
self.env.stop()
del self.env
return
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
class NeuralSimulation(object):
"""NeuralSimulation class
Responsible for initialization of all children objects such as the
Open Dynamics Engine environment and OpenGL viewer. Starts the main
event loop with real-time constraints.
Attributes:
env: The Open Dynamics Engine environment.
viewer: The OpenGL viewer for the ODE environment.
Methods:
start: Begins the main event loop.
"""
env = None
viewer = None
rnn = None
kinematics = None
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 start(self, fps, fast_step=False):
"""Start
Begin the continuous event loop for the simulation. This event loop
can be exited using the ctrl+c keyboard interrupt. Real-time
constraints are enforced. [Hz]
Arguments:
fps: The value of frames per second of the simulation.
fast_step: If True, the ODE fast step algorithm will be used.
This is faster and requires less memory but is less accurate.
(Default: False)
"""
paused = False
stopped = False
# Define the total time for the tooltip traversal
t_total = 20.0
# Define the simulation frame rate
t = 0.0 # [s]
dt = 1.0 / float(fps) # [s]
# Keep track of time overshoot in the case that simulation time must be
# increased in order to maintain real-time constraints
t_overshoot = 0.0
# Get the initial path position (center of gate7)
pos_start = self.env.get_body_pos('gate7') # [m]
# Get the first position of the PA10 at rest
pos_init = self.env.get_body_pos('tooltip') # [m]
# Calculate the new required joint angles of the PA10
#pa10_joint_angles = self.kinematics.calc_inverse_kinematics(pos_init, pos_start)
# TODO: Move the PA10 end-effector to the starting position along the path
# TODO: TEMP - Move the temporary end-effector pointer to the starting position
self.env.set_group_pos('pointer', pos_start)
# Generate long-term path from initial position
t_input = np.linspace(start=0.0, stop=1.0, num=t_total/dt)
t_input = np.reshape(t_input, (len(t_input), 1))
rnn_path = self.rnn.extrapolate(t_input, [pos_start], len(t_input)-1)
# Add the initial condition point back onto the data
rnn_path = np.vstack((pos_start, rnn_path))
# Retrieve one set of standard gate position/orientation data
file_path = pathutils.list_data_files(constants.G_TRAINING_DATA_DIR)[0]
gate_data = datastore.retrieve(file_path)
gate_start_idx = constants.G_GATE_IDX
gate_end_idx = gate_start_idx + constants.G_NUM_GATE_INPUTS
# Reshape the gate positions data
gate_data = gate_data[0:1,gate_start_idx:gate_end_idx]
gate_data = np.tile(gate_data, (len(rnn_path), 1))
# Complete the rnn path data
rnn_path = np.hstack((t_input, gate_data, rnn_path))
# Save generated path for later examination
datastore.store(rnn_path, constants.G_RNN_STATIC_PATH_OUT)
# Define a variable to hold the final path (with real-time correction)
final_path = rnn_path[:-1].copy()
path_saved = False
# Detect all path segments between gates in the generated path
segments = pathutils._detect_segments(rnn_path)
path_idx = 0
x_path_offset = np.array([0.0, 0.0, 0.0]) # [m]
v_curr = np.array([0.0, 0.0, 0.0]) # [m/s]
a_max = constants.G_MAX_ACCEL # [m/s^2]
# Get the static table position
x_table = self.env.get_body_pos('table')
while not stopped:
t_start = time.time()
# If the last calculation took too long, catch up
dt_warped = dt + t_overshoot
self.env.set_dt(dt_warped)
# Determine if the viewer is stopped. Then we can quit
if self.viewer.is_dead:
break
# Pause the simulation if we are at the end
if path_idx == len(rnn_path) - 1 or paused:
self.env.step(paused=True, fast=fast_step)
# If we have really hit the end of the simulation, save/plot the path
if not paused and not path_saved:
# Save the final data to a file
datastore.store(final_path, constants.G_RNN_DYNAMIC_PATH_OUT)
path_saved = True
continue
# Not a very elegant solution to pausing at the start, but it works
if t <= 1000.0:
self.env.step(paused=True, fast=fast_step)
t += dt_warped
continue
# Determine the current path segment
curr_segment_idx = 0
for segment_idx, segment_end in enumerate(segments):
if path_idx <= segment_end:
curr_segment_idx = segment_idx
break
x_curr = pathutils.get_path_tooltip_pos(rnn_path, path_idx) + x_path_offset
x_next = pathutils.get_path_tooltip_pos(rnn_path, path_idx+1) + x_path_offset
# Get the expected gate position
x_gate_expected = pathutils.get_path_gate_pos(
rnn_path,
segments[curr_segment_idx],
curr_segment_idx
)
# Get the actual gate position
x_gate_actual = self.env.get_body_pos('gate%d'%curr_segment_idx)
# Calculate the new position from change to new gate position
dx_gate = x_gate_actual - (x_gate_expected + x_path_offset)
x_new = x_next + dx_gate
# Calculate the new velocity
v_new = (x_new - x_curr) / dt_warped
# Calculate the new acceleration
a_new = (v_new - v_curr) / dt_warped
# Calculate the acceleration vector norm
a_new_norm = np.linalg.norm(a_new)
# Limit the norm vector
a_new_norm_clipped = np.clip(a_new_norm, -a_max, a_max)
# Determine the ratio of the clipped norm, protect against divide by zero
if a_new_norm != 0.0:
ratio_unclipped = a_new_norm_clipped / a_new_norm
else:
ratio_unclipped = 0.0
# Scale the acceleration vector by this ratio
a_new = a_new * ratio_unclipped
# Calculate the new change in velocity
dv_new = a_new * dt_warped
v_new = v_curr + dv_new
# Calculate the new change in position
dx_new = v_new * dt_warped
x_new = x_curr + dx_new
# Modify final path data with current tooltip and gate positions
pathutils.set_path_time(final_path, path_idx, t)
pathutils.set_path_tooltip_pos(final_path, path_idx, x_curr)
for gate_idx in range(constants.G_NUM_GATES):
gate_name = 'gate%d' % gate_idx
x_gate = self.env.get_body_pos(gate_name)
pathutils.set_path_gate_pos(final_path, path_idx, gate_idx, x_gate)
# Store this velocity for the next time step
v_curr = v_new
# Recalculate the current offset
x_path_offset += x_new - x_next
# Perform inverse kinematics to get joint angles
pa10_joint_angles = self.kinematics.calc_inverse_kinematics(x_curr, x_new)
# TODO: TEMP - MOVE ONLY POINTER, NO PA10
self.env.set_group_pos('pointer', x_new)
if constants.G_TABLE_IS_OSCILLATING:
# Move the table with y-axis oscillation
x_table_next = shaker_table(t, x_table)
else:
x_table_next = x_table
self.env.set_body_pos('table', x_table_next)
# Step through the world by 1 time frame and actuate pa10 joints
self.env.performAction(pa10_joint_angles, fast=fast_step)
# Update current time after this step
t += dt_warped
path_idx += 1
# Determine the difference in virtual vs actual time
t_warped = dt - (time.time() - t_start)
# Attempt to enforce real-time constraints
if t_warped >= 0.0:
# The calculation took less time than the virtual time. Sleep
# the rest off
time.sleep(t_warped)
t_overshoot = 0.0
else:
# The calculation took more time than the virtual time. We need
# to catch up with the virtual time on the next time step
t_overshoot = -t_warped
return
def __del__(self):
"""Delete (del)
Attempts to shut down any active engines and kills off spawned
class objects.
"""
# Kill the OpenGL viewer process
if self.viewer is not None:
self.viewer.stop()
del self.viewer
# Kill the ODE environment objects
if self.env is not None:
self.env.stop()
del self.env
return
