def get_fitness(string):
""" Run the simulation and return the fitness """
state_machine = sm.StateMachine()
behavior_tree = PyTree(string[:],
behaviors=behaviors,
world_interface=state_machine)
# run the Behavior Tree
behavior_tree.run_bt()
return fitness_function.compute_fitness(state_machine, behavior_tree)
def get_fitness(self, individual, save_video=False):
"""
Run the simulation and return the fitness
In case of error, restarts world_interface and tries again.
"""
pytree = PyTree(individual[:],
behaviors=behaviors,
world_interface=self.world_interface,
verbose=self.verbose)
status_ok = True
fitness = None
while fitness is None:
if self.counter >= self.maximum_runs or not status_ok:
self.world_interface.restart()
self.counter = 0
else:
self.world_interface.reset()
self.counter += 1
if save_video:
self.world_interface.start_video()
ticks, status_ok = pytree.run_bt(max_ticks=200)
count = 0
while count < 50 and status_ok: #Wait up to five seconds for everything to come to a resting state
old_sensor_data = self.world_interface.get_sensor_data()
status_ok = self.world_interface.get_feedback()
if status_ok:
if self.world_interface.at_standstill(old_sensor_data):
break
self.world_interface.send_references()
count += 1
if save_video:
time.sleep(1) #Needed for ros synch
self.world_interface.stop_video()
if status_ok:
fitness = fitness_function.compute_fitness(self.world_interface, pytree, ticks, \
self.targets, self.fitness_coeff, self.verbose)
else:
self.world_interface.restart()
self.counter = 0
print("Failed:", individual)
fitness = -9999999
return fitness
def plan(world_interface, behaviors, goals):
"""
Main planner function
Generates a behaviors tree to solve task given a set of goals
and behaviors with preconditions and postconditions. Since the
conditions are not always static, it actually runs the tree while evaluating
the conditions.
"""
tree = RSequence()
for goal in goals:
goal_condition, _ = behaviors.get_node_from_string(
goal, world_interface, [])
tree.add_child(goal_condition)
print(pt.display.unicode_tree(root=tree, show_status=True))
for i in range(60):
tree.tick_once()
print("Tick: ", i)
print(pt.display.unicode_tree(root=tree, show_status=True))
if tree.status is pt.common.Status.FAILURE:
expand_tree(tree, behaviors, world_interface)
print(pt.display.unicode_tree(root=tree, show_status=True))
elif tree.status is pt.common.Status.SUCCESS:
break
pt.display.render_dot_tree(tree, name='Planned bt', target_directory='')
print(PyTree('', behaviors, world_interface, tree).get_bt_from_root())
def get_fitness(self, individual):
""" Run the simulation and return the fitness """
state_machine = sm.StateMachine(self.start_positions,
self.random_events, self.sm_pars,
self.mode)
pytree = PyTree(individual[:],
behaviors=behaviors,
world_interface=state_machine,
verbose=self.verbose)
# run the Behavior Tree
ticks, _ = pytree.run_bt()
return fitness_function.compute_fitness(state_machine, pytree, ticks,
self.targets,
self.fitness_coeff)
def plot_individual(self, path, plot_name, individual):
""" Saves a graphical representation of the individual """
if self.static_tree is not None:
pytree = PyTree(self.add_to_static_tree(individual),
behaviors=behaviors)
else:
pytree = PyTree(individual[:], behaviors=behaviors)
pytree.save_fig(path, name=plot_name)
def plot_individual(path, plot_name, individual):
""" Saves a graphical representation of the individual """
pytree = PyTree(individual[:], behaviors=behaviors)
pytree.save_fig(path, name=plot_name)
def paper_figures():
# pylint: disable=import-outside-toplevel
""" Creates the bt figures used in the paper """
from behavior_tree_learning.py_trees_interface import PyTree
import behavior_tree_learning.behaviors_figures as behaviors
behavior_tree.load_settings_from_file('BT_SETTINGS_TOWER.yaml')
# Tower planned
tree = ['s(', 'f(', 'Green at A?', 's(', 'f(', 'picked Green?', 'pick Green!', ')', 'place at A!', ')', ')', \
'f(', 'Blue at pos B?', 's(', 'f(', 'Blue on Green?', 's(', 'f(', 'picked Blue?', 'pick Blue!', ')', 'place on Green!', ')', ')', 'apply force Blue!', ')', ')', \
'f(', 'Red at pos C?', 's(', 'f(', 'Red on Blue?', 's(', 'f(', 'picked Red?', 'pick Red!', ')', 'place on Blue!', ')', ')', 'apply force Red!', ')', ')', ')']
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='tower_planned', svg=True)
# Tower solved
tree = ['s(', 'f(', 'Green at pos A?', 's(', 'pick Green!', 'place at A!', ')', ')', \
'f(', 'Blue at pos B?', 's(', 'f(', 'Blue on Green?', 's(', 'pick Blue!', 'place on Green!', ')', ')', 'apply force Blue!', ')', ')', \
'f(', 'Red at pos C?', 's(', 'f(', 'Red on Blue?', 's(', 'pick Red!', 'place on Blue!', ')', ')', 'apply force Red!', ')', ')', ')']
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='tower_solved', svg=True)
behavior_tree.load_settings_from_file('BT_SETTINGS_CROISSANT.yaml')
# Croissant planned
tree = ['s(', 'f(', 'Green at pos B?', 's(', 'f(', 'picked Green?', 'pick Green!', ')', 'place at B!', ')', ')', \
'f(', 'Blue at pos D?', 's(', 'f(', 'Blue on Green?', 's(', 'f(', 'picked Blue?', 'pick Blue!', ')', 'place on Green!', ')', ')', 'apply force Blue!', ')', ')', \
'f(', 'Red at pos A?', 's(', 'f(', 'picked Red?', 'pick Red!', ')', 'place at A!', ')', ')', \
'f(', 'Yellow at pos C?', 's(', 'f(', 'picked Yellow?', 'pick Yellow!', ')', 'place at C!', ')', ')', ')']
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='croissant_planned', svg=True)
# Croissant solved
tree = ['s(', 'f(', 'Green at pos B?', 's(', 'pick Green!', 'place at B!', ')', ')', \
'f(', 'Red at pos A?', 's(', 'pick Red!', 'place at A!', ')', ')', \
'f(', 'Yellow at pos C?', 's(', 'pick Yellow!', 'place at C!', ')', ')', \
'f(', 'Blue at pos D?', 's(', 'f(', 'Blue on Green?', 's(', 'pick Blue!', 'place on Green!', ')', ')', 'apply force Blue!', ')', ')', ')']
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='croissant_solved', svg=True)
behavior_tree.load_settings_from_file('BT_SETTINGS_BALANCE.yaml')
# Balance planned
tree = [
's(', 'f(', 'Green at pos B?', 's(', 'f(', 'Blue at pos A?',
'put Blue at A!', ')', 'f(', 'Green on Blue?', 'put Green on Blue!',
')', 'apply force Green!', ')', ')', ')'
]
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='balance_planned', svg=True)
# Balance solved
tree = [
'f(', 'Green at pos B?', 's(', 'put Red at A!', 'put Green on Red!',
'apply force Green!', ')', ')'
]
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='balance_solved', svg=True)
behavior_tree.load_settings_from_file('BT_SETTINGS_BLOCKING.yaml')
# Blocking planned
tree = ['s(', 'f(', 'Green at A?', 'put Green at A!', ')', \
'f(', 'Blue at E?', 's(', 'f(', 'Blue on Green?', 'put Blue on Green!', ')', 'apply force Blue!', ')', ')', \
'f(', 'Red at C?', 'put Red at C!', ')', ')']
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='blocking_planned', svg=True)
# Blocking solved
tree = ['s(', 'put Blue on Green!', 'f(', 'Green at A?', 'put Red at D!', ')', \
'put Green at A!', \
'f(', 'Blue at E?', 'apply force Blue!', ')', \
'put Red at C!', ')']
pytree = PyTree(tree[:], behaviors=behaviors)
pytree.save_fig('logs/paperfigures/', name='blocking_solved', svg=True)