def run_experiment(self):
"""
@brief Run the whole experiment. Does num_ga_iters genetic algorithm runs each containing num_atr iterations
of ATR.
"""
#Run through a bunch of iterations
generations = ([1]*self.config['atr_iterations'] + [2])*self.config['ga_iterations']
print "%s generations: {%s}"%(len(generations),', '.join(str(generation) for generation in generations))
#Start it up
print "Running vanilla generation 0"
self.run_remote_dispatcher_tasks()
print "Generation %i complete"%self.gm.generation
for generation,gen_type in enumerate(generations,1):
#Get the resulting grasps for the latest generation of hands
grasp_list = self.gm.get_all_grasps()
#self.output_current_status()
#Every num_atr_iters+1th iteration is a genetic swap
if gen_type == 1:
#Run atr on the existing hand for the latest generation of grasps
new_hand_list = atr.ATR_generation(grasp_list, self.gm.hands)
print "New hands generated through ATR on the results of generation %i"%self.gm.generation
elif gen_type == 2:
#Generate new hands based on these grasps, scaling the variance of the mutations down linearly as the
#generations progress.
new_hand_list = eigenhand_genetic_algorithm.GA_generation(grasp_list, self.gm.hands, self.eval_functor, .5-.4/(self.config['ga_iterations']*generation/(1+self.config['atr_iterations'])))
print "New hands generated through genetic algorithm on the results of generation %i"%self.gm.generation
#Put the new hands in to the database.
eigenhand_db_tools.insert_unique_hand_list(new_hand_list, self.interface)
#Backup results and then remove everything from the grasp table
self.backup_results()
self.interface.clear_tables(['grasp','job'])
print "Backed up and reset for new generation"
#Run the planner to get grasps for the last set of hands
self.gm.next_generation(gen_type=gen_type)
print "Starting generation %i"%self.gm.generation
#Run the planning jobs
self.run_remote_dispatcher_tasks()
print "Finished generation %i"%self.gm.generation
#Output the newest set of results
thread.start_new_thread(output_results,[self.config['name']])
self.backup_results()
print "Backed up final results"
self.rd.kill_all_servers()
print "Killed all servers"
def run_experiment(self):
"""
@brief Run the whole experiment. Does num_ga_iters genetic algorithm runs each containing num_atr iterations
of ATR.
"""
#initialize new generation manager to configure the database to start running.
self.initialize_generation_manager()
self.gm.start_generation()
for ga_gen_num in range(self.starting_ga_iter, self.num_ga_iters):
for atr_gen_num in range(self.num_atr_iters):
#Doing ATR
#Run the planning jobs
self.run_remote_dispatcher_tasks()
#Get the resulting grasps for the latest generation of hands
grasp_list = self.gm.get_all_grasps()
self.output_current_status()
#Run atr on the existing hand for the latest generation of grasps
new_hand_list = atr.ATR_generation(grasp_list, self.gm.hands)
#Update database with new hands
eigenhand_db_tools.insert_unique_hand_list(new_hand_list, self.db_interface)
#[hand.generation.append(self.gm.generation + 1) for hand in self.gm.hands]
#[self.db_interface.update_hand_generation(hand) for hand in self.gm.hands]
# Get the database ready to perform jobs for the next generation
self.gm.next_generation()
#DOING GA
#run the planning jobs
self.run_remote_dispatcher_tasks()
#Get the resulting grasps for the latest generation of hands
grasp_list = self.gm.get_all_grasps()
self.output_current_status()
#Generate new hands based on these grasps, scaling the variance of the mutations down linearly as the
#generations progress.
new_hand_list = eigenhand_genetic_algorithm.GA_generation(grasp_list, self.gm.hands, self.eval_functor, .5-.4/self.num_ga_iters*ga_gen_num)
#Put the new hands in to the database.
eigenhand_db_tools.insert_unique_hand_list(new_hand_list, self.db_interface)
#[hand.generation.append(self.gm.generation + 1) for hand in self.gm.hands]
#[self.db_interface.update_hand_generation(hand) for hand in self.gm.hands]
#Backup old grasps and remove them from the grasp table
self.backup_grasps(self.gm.generation)
#Run the planner to get grasps for the last set of hands
self.gm.next_generation()
#Plan grasps for the final set of hands.
self.run_remote_dispatcher_tasks()
self.backup_grasps(self.gm.generation)
self.backup_hands()
def load_up_robot(hand, interface):
hand_list = [hand]
eigenhand_db_tools.insert_unique_hand_list(hand_list, interface)
print "New Hand ID: %d"%hand_list[0].hand_id
s = socket.socket()
s.connect(('localhost', 4765))
s.send('loadEigenhand %i \n'%(hand_list[0].hand_id))
output = s.recv(2048)
if int(output.split(" ")[0]) == 0:
raise ValueError("Could not load hand")
def load_up_robot(hand, interface):
hand_list = [hand]
eigenhand_db_tools.insert_unique_hand_list(hand_list, interface)
print "New Hand ID: %d" % hand_list[0].hand_id
s = socket.socket()
s.connect(('localhost', 4765))
s.send('loadEigenhand %i \n' % (hand_list[0].hand_id))
output = s.recv(2048)
if int(output.split(" ")[0]) == 0:
raise ValueError("Could not load hand")
def DB_test_split_phalange(hand = None, test_load = True):
interface = eigenhand_db_interface.EGHandDBaseInterface()
hands = interface.load_hands_for_generation(0)
if hand is None:
test_hand = hands[0]
else:
test_hand = hand
new_finger = eigenhand_genetic_algorithm.split_phalange(test_hand.fingers[0], 1)
new_hand = copy.deepcopy(test_hand)
new_hand.fingers[0] = new_finger
hand_list = [new_hand]
hand_list = eigenhand_db_tools.insert_unique_hand_list(hand_list, interface)
if test_load:
s = socket.socket()
s.connect(('localhost', 4765))
s.send('loadEigenhand %i \n'%(hand_list[0].hand_id))
output = s.recv(2048)
if int(output.split(" ")[0]) == 0:
raise ValueError("could not load hand")
return new_hand
