def control_loop(state, memory):
positions, velocities, rot_matrices = state
#sensor_values = engine.get_sensor_values(state=(positions, velocities, rot_matrices))
#objective_sensor = T.sum((target - positions[:,grapper_id,:])**2,axis=1)[:,None]
ALPHA = 0.95
if "recurrent" in controller:
network_input = T.concatenate([target, memory], axis=1)
controller["input"].input_var = network_input
memory = lasagne.layers.helper.get_output(controller["recurrent"])
memory = mulgrad(memory, ALPHA)
else:
network_input = T.concatenate([target], axis=1)
controller["input"].input_var = network_input
memory = None
motor_signals = lasagne.layers.helper.get_output(controller["output"])
positions, velocities, rot_matrices = mulgrad(
positions, ALPHA), mulgrad(velocities,
ALPHA), mulgrad(rot_matrices, ALPHA)
newstate = engine.step_from_this_state(state=(positions, velocities,
rot_matrices),
motor_signals=motor_signals)
if "recurrent" in controller:
newstate += (memory, )
return newstate
def control_loop(state, memory):
positions, velocities, rot_matrices = state
#sensor_values = engine.get_sensor_values(state=(positions, velocities, rot_matrices))
ALPHA = 1.0
image = engine.get_camera_image(EngineState(*state), CAMERA)
controller["input"].input_var = image - 0.5 #for normalization
if "recurrent" in controller:
controller["memory"].input_var = memory
memory = lasagne.layers.helper.get_output(
controller["recurrent"], deterministic=deterministic)
memory = mulgrad(memory, ALPHA)
motor_signals = lasagne.layers.helper.get_output(
controller["output"], deterministic=deterministic)
positions, velocities, rot_matrices = mulgrad(
positions, ALPHA), mulgrad(velocities,
ALPHA), mulgrad(rot_matrices, ALPHA)
newstate = engine.do_time_step(state=EngineState(
positions, velocities, rot_matrices),
motor_signals=motor_signals)
newstate += (image, )
if "recurrent" in controller:
newstate += (memory, )
return newstate
def control_loop(state, memory):
positions, velocities, rot_matrices = state
#sensor_values = engine.get_sensor_values(state=(positions, velocities, rot_matrices))
objective_sensor = (target - positions[:, grapper_id, :]).norm(
L=2, axis=1)[:, None]
ALPHA = 1.0
if "recurrent" in controller:
controller["input"].input_var = objective_sensor
controller["memory"].input_var = memory
memory = lasagne.layers.helper.get_output(
controller["recurrent"], deterministic=deterministic)
memory = mulgrad(memory, ALPHA)
else:
network_input = T.concatenate([objective_sensor], axis=1)
controller["input"].input_var = network_input
memory = None
motor_signals = lasagne.layers.helper.get_output(
controller["output"], deterministic=deterministic)
positions, velocities, rot_matrices = mulgrad(
positions, ALPHA), mulgrad(velocities,
ALPHA), mulgrad(rot_matrices, ALPHA)
newstate = engine.step_from_this_state(state=(positions, velocities,
rot_matrices),
motor_signals=motor_signals)
if "recurrent" in controller:
newstate += (memory, )
return newstate
def control_loop(state, t):
positions, velocities, rot_matrices = state
#sensor_values = engine.get_sensor_values(state=(positions, velocities, rot_matrices))
t = t + engine.DT
sine = T.tile(T.sin(np.float32(2 * np.pi * 1.5) * t), BATCH_SIZE)
cosine = T.tile(T.cos(np.float32(2 * np.pi * 1.5) * t), BATCH_SIZE)
sensor_values = T.concatenate([sine[:, None], cosine[:, None]], axis=1)
controller["input"].input_var = sensor_values
motor_signals = lasagne.layers.helper.get_output(controller["output"])
ALPHA = 0.95
positions, velocities, rot_matrices = mulgrad(
positions, ALPHA), mulgrad(velocities,
ALPHA), mulgrad(rot_matrices, ALPHA)
return (t, ) + engine.step_from_this_state(
state=(positions, velocities, rot_matrices),
motor_signals=motor_signals)
