def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('apex_playground'), 'config',
'learning.json')) as f:
self.params = json.load(f)
self.translator = EnvironmentTranslator()
self.learning = None
# User control and critical resources
self.lock_iteration = RLock()
self.ready_for_interaction = True
self.focus = None
self.set_iteration = -1
# Saved experiment files
self.dir = join(self.rospack.get_path('apex_playground'), 'logs')
if not os.path.isdir(self.dir):
os.makedirs(self.dir)
# self.stamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
# self.source_file = join(self.dir, self.source_name + '.pickle')
self.main_experiment = True
self.condition = ""
self.trial = ""
self.task = ""
# Serving these services
self.service_name_perceive = "learning/perceive"
self.service_name_produce = "learning/produce"
self.service_name_set_interest = "learning/set_interest"
self.service_name_set_iteration = "learning/set_iteration"
self.service_name_interests = "learning/get_interests"
# Publishing these topics
self.pub_focus = rospy.Publisher('learning/current_focus',
String,
queue_size=1,
latch=True)
self.pub_user_focus = rospy.Publisher('learning/user_focus',
String,
queue_size=1,
latch=True)
self.pub_ready = rospy.Publisher('learning/ready_for_interaction',
Bool,
queue_size=1,
latch=True)
self.pub_iteration = rospy.Publisher('iteration',
UInt32,
queue_size=1,
latch=True)
# Using these services
self.service_name_get_perception = "perception/get"
for service in [self.service_name_get_perception]:
rospy.loginfo(
"Learning node is waiting service {}...".format(service))
rospy.wait_for_service(service)
self.get_state = rospy.ServiceProxy(self.service_name_get_perception,
GetSensorialState)
def __init__(self):
# Register Ctrl-C sigint
signal.signal(signal.SIGINT, self._signal_handler)
rpack = RosPack()
pkg_path = rpack.get_path('dialogflow_ros')
rospy.Subscriber('/bat_charge', Float64, self.cbk_bat_charge)
# -------------------------------------------------- #
# COSTRUISCO IL PANNELLO
# ---------------------------------------------- #
global app
app = QApplication(sys.argv)
#MainWindow = AppMainWindow()
win = QMainWindow()
win.resize(800, 600)
BtnLayout = QtWidgets.QHBoxLayout()
self.PlusButton = QtWidgets.QPushButton('+')
self.MinusButton = QtWidgets.QPushButton('-')
BtnLayout.addWidget(self.PlusButton)
BtnLayout.addWidget(self.MinusButton)
button = QPushButton('Click')
button.clicked.connect(self.on_button_clicked)
button.show()
app.exec_()
def __init__(self):
self.interrupted = False
self.detector = None
rpack = RosPack()
# UMDL or PMDL file paths along with audio files
pkg_path = rpack.get_path('dialogflow_ros')
#self.model_path = pkg_path + '/scripts/snowboy/resources/jarvis.umdl'
self.model_path = pkg_path + '/scripts/snowboy/resources/OK_Robot.pmdl'
print("Hotoword: {}".format(self.model_path))
ding_path = pkg_path + '/scripts/snowboy/resources/ding.wav'
# Setup df
self.df_client = None
# Setup audio output
ding = wave.open(ding_path, 'rb')
self.ding_data = ding.readframes(ding.getnframes())
self.audio = pyaudio.PyAudio()
self.stream_out = self.audio.open(
format=self.audio.get_format_from_width(ding.getsampwidth()),
channels=ding.getnchannels(),
rate=ding.getframerate(),
input=False,
output=True)
self.last_contexts = []
rospy.loginfo("HOTWORD_CLIENT: Ready!")
def __init__(self):
self.rospack = RosPack()
with open(join(self.rospack.get_path('nips2016'), 'config', 'learning.json')) as f:
self.params = json.load(f)
self.translator = EnvironmentTranslator()
self.learning = Learning(self.translator.config,
n_motor_babbling=self.params["n_motor_babbling"],
explo_noise=self.params["explo_noise"],
choice_eps=self.params["choice_eps"],
enable_hand=self.params["enable_hand"],
normalize_interests=self.params["normalize_interests"])
self.experiment_name = rospy.get_param("/nips2016/experiment_name", "experiment")
# self.source_name = rospy.get_param("/nips2016/source_name", "experiment")
rospy.loginfo("Learning node will write {}".format(self.experiment_name))
# rospy.loginfo("Learning node will read {}".format(self.source_name))
# User control and critical resources
self.lock_iteration = RLock()
self.ready_for_interaction = True
self.focus = None
self.set_iteration = -1
self.demonstrate = None
# Saved experiment files
self.dir = join(self.rospack.get_path('nips2016'), 'logs')
if not os.path.isdir(self.dir):
os.makedirs(self.dir)
# self.stamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
# self.source_file = join(self.dir, self.source_name + '.pickle')
self.experiment_file = join(self.dir, self.experiment_name + '.pickle') # if self.source_name == "none" else self.source_file
self.main_experiment = True
if isfile(self.experiment_file):
self.learning.restart_from_end_of_file(self.experiment_file)
else:
self.learning.start()
# Serving these services
self.service_name_perceive = "/nips2016/learning/perceive"
self.service_name_produce = "/nips2016/learning/produce"
self.service_name_set_interest = "/nips2016/learning/set_interest"
self.service_name_set_iteration = "/nips2016/learning/set_iteration"
self.service_name_demonstrate = "/nips2016/learning/assess"
# Publishing these topics
self.pub_interests = rospy.Publisher('/nips2016/learning/interests', Interests, queue_size=1, latch=True)
self.pub_focus = rospy.Publisher('/nips2016/learning/current_focus', String, queue_size=1, latch=True)
self.pub_user_focus = rospy.Publisher('/nips2016/learning/user_focus', String, queue_size=1, latch=True)
self.pub_ready = rospy.Publisher('/nips2016/learning/ready_for_interaction', Bool, queue_size=1, latch=True)
self.pub_iteration = rospy.Publisher('/nips2016/iteration', UInt32, queue_size=1, latch=True)
# Using these services
self.service_name_get_perception = "/nips2016/perception/get"
for service in [self.service_name_get_perception]:
rospy.loginfo("Learning node is waiting service {}...".format(service))
rospy.wait_for_service(service)
self.get_state = rospy.ServiceProxy(self.service_name_get_perception, GetSensorialState)
def create_application(self, argv):
from python_qt_binding.QtGui import QIcon
app = super(Main, self).create_application(argv)
rp = RosPack()
logo = os.path.join(rp.get_path('rqt_gui'), 'resource', 'rqt.svg')
icon = QIcon(logo)
app.setWindowIcon(icon)
return app
def __init__(self, title='Options', description=None):
super(SimpleSettingsDialog, self).__init__()
self.setObjectName('SimpleSettingsDialog')
rp = RosPack()
ui_file = os.path.join(rp.get_path('rqt_quaternion_view'), 'resource',
'QuaternionViewOptions.ui')
loadUi(ui_file, self)
self.setWindowTitle(title)
if description is not None:
self.add_label(description)
def __init__(self, title='Options', description=None):
super(SimpleSettingsDialog, self).__init__()
self.setObjectName('SimpleSettingsDialog')
rp = RosPack()
ui_file = os.path.join(rp.get_path('qt_gui_py_common'), 'resource', 'simple_settings_dialog.ui')
loadUi(ui_file, self)
self.setWindowTitle(title)
self._settings_groups = []
if description is not None:
self.add_label(description)
def __init__(self):
self._action_name = '/pobax_playground/perception/record_server'
self._as = actionlib.SimpleActionServer(self._action_name,
RecordAction,
execute_cb=self.execute_cb,
auto_start=False)
self._as.start()
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'perception.json')) as f:
self.params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
self.topics = TopicAggregator()
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('nips2016'), 'config',
'perception.json')) as f:
self.params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
# Serving these services
self.service_name_get = "/nips2016/perception/get"
self.service_name_record = "/nips2016/perception/record"
# Using these services
self.service_name_set_compliant = "/nips2016/torso/set_compliant"
self.topics = TopicAggregator(
) # All topics are read and stored in that object
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'perception.json')) as f:
self.params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
# Serving these services
self.service_name_get = "/pobax_playground/perception/get"
self.service_name_record = "/pobax_playground/perception/record"
#self.service_name_baxter_start_record = "/pobax_playground/perception/baxter_start_record"
#self.service_name_baxter_stop_record = "/pobax_playground/perception/baxter_stop_record"
# Using these services
self.topics = TopicAggregator(
) # All topics are read and stored in that object
def __init__(self, filename=None, ros_pack=None):
rp = ros_pack or RosPack()
qtgui_path = rp.get_path('qt_gui')
super(Main, self).__init__(qtgui_path,
invoked_filename=filename,
settings_filename='rqt_gui')
self._ros_pack = rp
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'voice.json')) as f:
self.params = json.load(f)
# Serving these services
self.service_name_execute_analyse = "/pobax_playground/voice/execute_analyse"
self.service_name_baxter_analyse = "/pobax_playground/voice/baxter_analyse"
# Init DIVA vocal tract simulator
self.voice = Voice(self.params["tau"],
self.params["pa"],
self.params["pc"],
gui=self.params["gui"],
audio=self.params["audio"])
def handle_rwt_action(req):
action = rosaction.MODE_ACTION
pkg = RosPack()
package = rosaction.iterate_packages(pkg, action)
messageList = []
for pack in package:
action, path = pack
message = rosmsg.list_msgs(action)
messageList.append(str(message))
return ActionListResponse(messageList)
def handle_rwt_srv(req):
srv = rosmsg.MODE_SRV
pkg = RosPack()
package = rosmsg.iterate_packages(pkg, srv)
messageList = []
for pack in package:
service, path = pack
message = rosmsg.list_srvs(service)
messageList.append(str(message))
return SrvListResponse(messageList)
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('apex_playground'), 'config',
'general.json')) as f:
self.params = json.load(f)
with open(
join(self.rospack.get_path('apex_playground'), 'config',
'torso.json')) as f:
self.torso_params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
# Serving these services
self.service_name_get = "perception/get"
self.service_name_record = "perception/record"
# Using these services
self.service_name_set_compliant = "{}/left_arm/set_compliant".format(
self.torso_params["robot_name"])
self.topics = PerceptionServices(
) # All topics are read and stored in that object
class Perception(object):
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'perception.json')) as f:
self.params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
# Serving these services
self.service_name_get = "/pobax_playground/perception/get"
self.service_name_record = "/pobax_playground/perception/record"
#self.service_name_baxter_start_record = "/pobax_playground/perception/baxter_start_record"
#self.service_name_baxter_stop_record = "/pobax_playground/perception/baxter_stop_record"
# Using these services
self.topics = TopicAggregator(
) # All topics are read and stored in that object
def run(self):
rospy.Service(self.service_name_get, GetSensorialState, self.cb_get)
rospy.Service(self.service_name_record, Record, self.cb_record)
#rospy.Service(self.service_name_baxter_start_record, BaxterStartRecord, self.cb_baxter_start_record)
#rospy.Service(self.service_name_baxter_stop_record, BaxterStopRecord, self.cb_baxter_stop_record)
rospy.loginfo("Done, perception is up!")
def get(self):
#TODO ADD VOICE
state = SensorialState(hand=self.topics.torso_l_eef,
culbuto_1=self.topics.culbuto_1)
return state
################################# Service callbacks
def cb_get(self, request):
return GetSensorialStateResponse(state=self.get())
def cb_record(self, request):
response = RecordResponse()
# TODO eventually keep trace of the last XX points to start recording prior to the start signal
# TODO add VOICE DEMO
rospy.loginfo("Recording {}...".format("an arm demo"))
for point in range(request.nb_points.data):
if rospy.is_shutdown():
break
response.sensorial_trajectory.points.append(self.get())
self.rate.sleep()
rospy.loginfo("Recorded!")
return response
'''
class VoiceNode(object):
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'voice.json')) as f:
self.params = json.load(f)
# Serving these services
self.service_name_execute_analyse = "/pobax_playground/voice/execute_analyse"
self.service_name_baxter_analyse = "/pobax_playground/voice/baxter_analyse"
# Init DIVA vocal tract simulator
self.voice = Voice(self.params["tau"],
self.params["pa"],
self.params["pc"],
gui=self.params["gui"],
audio=self.params["audio"])
def run(self):
rospy.Service(self.service_name_execute_analyse,
ExecuteAnalyseVocalTrajectory, self.cb_execute_analyse)
rospy.Service(self.service_name_baxter_analyse,
BaxterAnalyseVocalTrajectory, self.cb_baxter_analyse)
def cb_baxter_analyse(self, request):
rospy.loginfo(
'Voice node analysing baxter sound response to torso movement')
baxter_sound_traj = self.voice.baxter_analyse(
request.is_culbuto_touched)
return BaxterAnalyseVocalTrajectoryResponse(
baxter_sound_trajectory=SoundTrajectory(data=baxter_sound_traj))
def cb_execute_analyse(self, request):
rospy.loginfo('Voice node producing sound...')
torso_sound_traj, baxter_sound_traj, is_culb_name, produced_name, raw_torso_sound_traj\
= self.voice.execute_analyse(request.vocal_trajectory.data)
return ExecuteAnalyseVocalTrajectoryResponse(
torso_sound_trajectory=SoundTrajectory(data=torso_sound_traj),
baxter_sound_trajectory=SoundTrajectory(data=baxter_sound_traj),
is_culbuto_name=is_culb_name,
produced_name=produced_name,
raw_torso_sound=SoundTrajectory(data=raw_torso_sound_traj))
class LearningNode(object):
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('apex_playground'), 'config',
'learning.json')) as f:
self.params = json.load(f)
self.translator = EnvironmentTranslator()
self.learning = None
# User control and critical resources
self.lock_iteration = RLock()
self.ready_for_interaction = True
self.focus = None
self.set_iteration = -1
self.demonstrate = None
# Saved experiment files
self.dir = join(self.rospack.get_path('apex_playground'), 'logs')
if not os.path.isdir(self.dir):
os.makedirs(self.dir)
# self.stamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
# self.source_file = join(self.dir, self.source_name + '.pickle')
self.main_experiment = True
self.condition = ""
self.trial = ""
# Serving these services
self.service_name_perceive = "learning/perceive"
self.service_name_produce = "learning/produce"
self.service_name_set_interest = "learning/set_interest"
self.service_name_set_iteration = "learning/set_iteration"
self.service_name_demonstrate = "learning/assess"
self.service_name_interests = "learning/get_interests"
# Publishing these topics
self.pub_focus = rospy.Publisher('learning/current_focus',
String,
queue_size=1,
latch=True)
self.pub_user_focus = rospy.Publisher('learning/user_focus',
String,
queue_size=1,
latch=True)
self.pub_ready = rospy.Publisher('learning/ready_for_interaction',
Bool,
queue_size=1,
latch=True)
self.pub_iteration = rospy.Publisher('iteration',
UInt32,
queue_size=1,
latch=True)
# Using these services
self.service_name_get_perception = "perception/get"
for service in [self.service_name_get_perception]:
rospy.loginfo(
"Learning node is waiting service {}...".format(service))
rospy.wait_for_service(service)
self.get_state = rospy.ServiceProxy(self.service_name_get_perception,
GetSensorialState)
def produce_init_learner(self):
condition = rospy.get_param('experiment/current/method')
trial = rospy.get_param('experiment/current/trial')
iteration = rospy.get_param('experiment/current/iteration')
experiment_name = rospy.get_param('/experiment/name')
if condition != self.condition or trial != self.trial:
with self.lock_iteration:
rospy.logwarn("Learner opens condition {} trial {}...".format(
condition, trial + 1))
self.learning = Learning(
self.translator.config,
condition=condition,
n_motor_babbling=self.params["n_motor_babbling"],
explo_noise=self.params["explo_noise"],
choice_eps=self.params["choice_eps"],
enable_hand=self.params["enable_hand"],
normalize_interests=self.params["normalize_interests"])
if iteration > 0:
rospy.loginfo(
"Learning node restarts {} from iteration {} trial {}".
format(experiment_name, iteration, trial))
self.learning.restart_from_files(experiment_name, trial,
iteration)
else:
rospy.loginfo(
"Learning node starts {} from scratch trial {}".format(
experiment_name, trial))
self.learning.start()
rospy.loginfo(
"Learner loaded with condition {}!".format(condition))
self.condition = condition
self.trial = trial
def cb_get_interests(self, request):
interests_array = self.learning.get_normalized_interests_evolution()
interests = GetInterestsResponse()
if self.learning is not None:
interests.names = self.learning.get_space_names()
interests.num_iterations = UInt32(len(interests_array))
interests.interests = [
Float32(val) for val in interests_array.flatten()
]
return interests
def run(self):
rospy.Service(self.service_name_perceive, Perceive, self.cb_perceive)
rospy.Service(self.service_name_produce, Produce, self.cb_produce)
rospy.Service(self.service_name_set_interest, SetFocus,
self.cb_set_focus)
rospy.Service(self.service_name_set_iteration, SetIteration,
self.cb_set_iteration)
rospy.Service(self.service_name_demonstrate, Assess, self.cb_assess)
rospy.Service(self.service_name_interests, GetInterests,
self.cb_get_interests)
rospy.loginfo("Learning is up!")
rate = rospy.Rate(self.params['publish_rate'])
while not rospy.is_shutdown():
self.publish()
rate.sleep()
def publish(self):
if self.learning is not None:
with self.lock_iteration:
focus = copy(self.focus)
ready = copy(self.ready_for_interaction)
self.pub_ready.publish(Bool(data=ready))
self.pub_user_focus.publish(
String(data=focus if focus is not None else ""))
self.pub_focus.publish(String(data=self.learning.get_last_focus()))
self.pub_iteration.publish(
UInt32(data=self.learning.get_iterations()))
################################# Service callbacks
def cb_set_iteration(self, request):
with self.lock_iteration:
ready = copy(self.ready_for_interaction)
self.ready_for_interaction = False
self.set_iteration = request.iteration.data
into_past = request.iteration.data < self.learning.get_iterations()
if ready:
if into_past:
pass
#if self.main_experiment:
# self.learning.save(experiment_name, self.trial)
#self.main_experiment = False
#rospy.loginfo("Saving file before time travel")
else:
self.main_experiment = True
return SetIterationResponse()
def cb_set_focus(self, request):
with self.lock_iteration:
# space is "" or e.g. "s_hand" to toggle focus on/off
self.focus = request.space if len(request.space) > 0 else None
self.ready_for_interaction = False
return SetFocusResponse()
def cb_assess(self, request):
with self.lock_iteration:
self.demonstrate = request.goal
self.ready_for_interaction = False
return AssessResponse()
def cb_perceive(self, request):
s = self.translator.sensory_trajectory_msg_to_list(
request.demo.sensorial_demonstration)
if request.demo.type_demo == Demonstration.TYPE_DEMO_ARM:
torso_traj = self.translator.trajectory_msg_to_matrix(
request.demo.torso_demonstration)
torso_traj_w = self.translator.trajectory_to_w(torso_traj)
rospy.loginfo(
"Learning node is perceiving sensory + torso trajectories for an arm demo"
)
success = self.learning.perceive(s, m_demo=torso_traj_w)
elif request.demo.type_demo == Demonstration.TYPE_DEMO_JOYSTICK:
rospy.loginfo(
"Learning node is perceiving sensory + torso trajectories for a joystick demo"
)
success = self.learning.perceive(s, j_demo=True)
else:
rospy.loginfo(
"Learning node is perceiving sensory trajectory only for a normal demo"
)
success = self.learning.perceive(s)
if not success:
rospy.logerr("Learner could not perceive this trajectory")
# This turn is over, check if we have a time travel pending...
with self.lock_iteration:
set_iteration = copy(self.set_iteration)
self.set_iteration = -1
if set_iteration > -1:
rospy.logwarn(
"Applying time travel to iteration {}".format(set_iteration))
#self.learning.restart_from_files(experiment_name, set_iteration)
# And savethe current iteration
experiment_name = rospy.get_param('/experiment/name')
self.learning.save(experiment_name, self.trial)
return PerceiveResponse()
def cb_produce(self, request):
with self.lock_iteration:
# Check if we need a new learner
self.produce_init_learner()
focus = copy(self.focus)
demonstrate = copy(self.demonstrate)
self.demonstrate = None
rospy.loginfo("Learning node is requesting the current state")
state = self.get_state(GetSensorialStateRequest()).state
if demonstrate is None:
rospy.loginfo("Learning node is producing...")
w = self.learning.produce(self.translator.get_context(state),
focus)
else:
rospy.logwarn("Assessing {}...".format(demonstrate))
context = self.translator.get_context(state)
w = self.learning.produce(context, goal=demonstrate)
trajectory_matrix = self.translator.w_to_trajectory(w)
trajectory_msg = self.translator.matrix_to_trajectory_msg(
trajectory_matrix)
self.ready_for_interaction = True
response = ProduceResponse(torso_trajectory=trajectory_msg)
return response
class Perception(object):
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('nips2016'), 'config',
'perception.json')) as f:
self.params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
# Serving these services
self.service_name_get = "/nips2016/perception/get"
self.service_name_record = "/nips2016/perception/record"
# Using these services
self.service_name_set_compliant = "/nips2016/torso/set_compliant"
self.topics = TopicAggregator(
) # All topics are read and stored in that object
def run(self):
for service in [self.service_name_set_compliant]:
rospy.loginfo("Perception is waiting service {}".format(service))
rospy.wait_for_service(service)
self.set_torso_compliant_srv = rospy.ServiceProxy(
self.service_name_set_compliant, SetTorsoCompliant)
rospy.Service(self.service_name_get, GetSensorialState, self.cb_get)
rospy.Service(self.service_name_record, Record, self.cb_record)
rospy.loginfo("Done, perception is up!")
def get(self):
state = SensorialState(ball=self.topics.ball,
ergo=self.topics.ergo,
color=self.topics.light,
sound=self.topics.sound,
joystick_1=self.topics.joy1,
joystick_2=self.topics.joy2,
hand=self.topics.torso_l_eef)
return state
def wait_for_human_interaction(self,
arm_threshold=1,
joystick_threshold=0.15):
rospy.loginfo("We are waiting for human interaction...")
def detect_arm_variation():
new_effort = np.array(self.topics.torso_l_j.effort)
delta = np.absolute(effort - new_effort)
return np.amax(delta) > arm_threshold
def detect_joy_variation():
return np.amax(np.abs(self.topics.joy1.axes)) > joystick_threshold
effort = np.array(self.topics.torso_l_j.effort)
rate = rospy.Rate(50)
is_joystick_demo = None
while not rospy.is_shutdown():
if detect_arm_variation():
is_joystick_demo = False
break
elif detect_joy_variation():
is_joystick_demo = True
break
rate.sleep()
return is_joystick_demo
################################# Service callbacks
def cb_get(self, request):
return GetSensorialStateResponse(state=self.get())
def cb_record(self, request):
response = RecordResponse()
# TODO eventually keep trace of the last XX points to start recording prior to the start signal
is_joystick_demo = False
if request.human_demo.data:
# Blocking... Wait for the user's grasp before recording...
is_joystick_demo = self.wait_for_human_interaction()
if not is_joystick_demo:
self.set_torso_compliant_srv(
SetTorsoCompliantRequest(compliant=True))
rospy.loginfo("Recording {}...".format(
"a joystick demo" if is_joystick_demo else "an arm demo"))
for point in range(request.nb_points.data):
if rospy.is_shutdown():
break
if point % self.params["divider_nb_points_sensory"] == 0:
response.demo.sensorial_demonstration.points.append(self.get())
if not is_joystick_demo:
response.demo.torso_demonstration.points.append(
joints.state_to_jtp(self.topics.torso_l_j))
self.rate.sleep()
if not is_joystick_demo:
self.set_torso_compliant_srv(
SetTorsoCompliantRequest(compliant=False))
if is_joystick_demo:
response.demo.type_demo = Demonstration.TYPE_DEMO_JOYSTICK
elif request.human_demo.data:
response.demo.type_demo = Demonstration.TYPE_DEMO_ARM
else:
response.demo.type_demo = Demonstration.TYPE_DEMO_NORMAL
rospy.loginfo("Recorded!")
return response
class Perception(object):
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('apex_playground'), 'config',
'general.json')) as f:
self.params = json.load(f)
with open(
join(self.rospack.get_path('apex_playground'), 'config',
'torso.json')) as f:
self.torso_params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
# Serving these services
self.service_name_get = "perception/get"
self.service_name_record = "perception/record"
# Using these services
self.service_name_set_compliant = "{}/left_arm/set_compliant".format(
self.torso_params["robot_name"])
self.topics = PerceptionServices(
) # All topics are read and stored in that object
def run(self):
for service in [self.service_name_set_compliant]:
rospy.loginfo("Perception is waiting service {}".format(service))
rospy.wait_for_service(service)
self.set_torso_compliant_srv = rospy.ServiceProxy(
self.service_name_set_compliant, SetTorsoCompliant)
rospy.Service(self.service_name_get, GetSensorialState, self.cb_get)
rospy.Service(self.service_name_record, Record, self.cb_record)
rospy.loginfo("Done, perception is up!")
rospy.spin()
def get(self):
state = SensorialState(ball=self.topics.ball,
ergo=self.topics.ergo,
color=self.topics.light,
sound=self.topics.sound,
joystick_1=self.topics.joy1,
joystick_2=self.topics.joy2,
hand=self.topics.torso_l_eef)
return state
def wait_for_human_interaction(self,
arm_threshold=1,
joystick_threshold=0.15):
rospy.loginfo("We are waiting for human interaction...")
def detect_arm_variation():
new_effort = np.array(self.topics.torso_l_j.effort)
delta = np.absolute(effort - new_effort)
return np.amax(delta) > arm_threshold
def detect_joy_variation():
return np.amax(np.abs(self.topics.joy1.axes)) > joystick_threshold
effort = np.array(self.topics.torso_l_j.effort)
rate = rospy.Rate(50)
is_joystick_demo = None
while not rospy.is_shutdown():
if detect_arm_variation():
is_joystick_demo = False
break
elif detect_joy_variation():
is_joystick_demo = True
break
rate.sleep()
return is_joystick_demo
################################# Service callbacks
def cb_get(self, request):
return GetSensorialStateResponse(state=self.get())
def cb_record(self, request):
response = RecordResponse()
# TODO eventually keep trace of the last XX points to start recording prior to the start signal
folder = rospy.get_param('/experiment/results_path',
'/home/pi/apex_results/')
condition = rospy.get_param('experiment/current/method')
trial = rospy.get_param('experiment/current/trial')
task = rospy.get_param('experiment/current/task')
iteration = rospy.get_param('experiment/current/iteration')
experiment_name = rospy.get_param('/experiment/name')
folder_trial = join(folder, experiment_name, "task_" + str(task),
"condition_" + str(condition),
"trial_" + str(trial))
folder_traj = join(folder_trial, 'trajectories')
if not isdir(folder_traj):
makedirs(folder_traj)
sensorial_points = []
motor_points = []
try:
is_joystick_demo = False
if request.human_demo.data:
# Blocking... Wait for the user's grasp before recording...
is_joystick_demo = self.wait_for_human_interaction()
if not is_joystick_demo:
self.set_torso_compliant_srv(
SetTorsoCompliantRequest(compliant=True))
rospy.loginfo("Recording {}...".format(
"a joystick demo" if is_joystick_demo else "an arm demo"))
for point in range(request.nb_points.data):
if rospy.is_shutdown():
break
sensorial_point = self.get()
motor_point = self.topics.torso_l_j
if point % self.params["divider_nb_points_sensory"] == 0:
response.demo.sensorial_demonstration.points.append(
sensorial_point)
if not is_joystick_demo:
response.demo.torso_demonstration.points.append(
joints.state_to_jtp(motor_point))
# Saving trajectories for file dumping
sensorial_points.append(sensorial_point)
motor_points.append(motor_point)
self.rate.sleep()
if not is_joystick_demo:
self.set_torso_compliant_srv(
SetTorsoCompliantRequest(compliant=False))
if is_joystick_demo:
response.demo.type_demo = Demonstration.TYPE_DEMO_JOYSTICK
type = "demo_joy"
elif request.human_demo.data:
response.demo.type_demo = Demonstration.TYPE_DEMO_ARM
type = "demo_arm"
else:
response.demo.type_demo = Demonstration.TYPE_DEMO_NORMAL
type = "normal"
trajectories = [{
"motor":
joints.ros_to_list(motor_points[point]),
"sensorial":
sensorial.ros_to_dict(sensorial_points[point]),
"type":
type
} for point in range(request.nb_points.data)]
#with open(join(folder_traj, "iteration_{}.json".format(iteration)), 'w') as f:
# json.dump(trajectories, f)
except rospy.exceptions.ROSInterruptException:
rospy.logwarn("recording aborted!")
except IOError:
# TODO: total or partial iteration failure?
raise
rospy.logerr("I/O error when dumping trajectories")
else:
rospy.loginfo("Recorded!")
return response
from std_msgs.msg import String, Float64, Bool
from geometry_msgs.msg import Twist
# ROS-Dialogflow
from dialogflow_ros.msg import *
#esegue i comandi riconosciuti da DialogFlow
from dialogflow_mycommands import DialogflowCommandExecutor
# Use to convert Struct messages to JSON
# from google.protobuf.json_format import MessageToJson
sys.path.append('/opt/ros/melodic/lib/python2.7/dist-packages'
) # in order to import cv2 under python3
rpack = RosPack()
# UMDL or PMDL file paths along with audio files
import wave
pkg_path = rpack.get_path('dialogflow_ros')
ding_path = pkg_path + '/scripts/snowboy/resources/ding.wav'
ding = wave.open(ding_path, 'rb')
def isNumber(s):
try:
float(s)
return True
except ValueError:
return False
class Voice(object):
def __init__(self, tau, pa, pc, gui=False, audio=False):
self.t = 0
self.tau = tau
self.pa = pa
self.pc = pc
# SOUND CONFIG
self.v_o = list(np.log2([500, 900]))
self.v_y = list(np.log2([300, 1700]))
self.v_u = list(np.log2([300, 800]))
self.v_e = list(np.log2([400, 2200]))
self.v_i = list(np.log2([300, 2300]))
# Retrieve caregiver sounds and trajectories from json
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'human_sounds.pickle')) as f:
self.full_human_motor_traj, self.full_human_sounds_traj = pickle.load(
f)
self.human_sounds = self.full_human_sounds_traj.keys()
rospy.loginfo('Voice node using the word %s for culbuto name' %
self.human_sounds[0])
self.vowels = dict(o=self.v_o,
y=self.v_y,
u=self.v_u,
e=self.v_e,
i=self.v_i)
self.human_sounds = ['eyu', 'oey', 'eou', 'oyi']
#random.shuffle(self.human_sounds)
print "human sounds", self.human_sounds
def compress_sound_traj(sound):
assert (len(sound) == 100)
f1s = sound[:50]
f3s = sound[50:]
return np.append(f1s[np.array([0, 12, 24, 37, 49])],
f3s[np.array([0, 12, 24, 37, 49])])
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compress_sound_traj(
self.full_human_sounds_traj[hs])
self.human_sounds_traj_std[hs] = [
d - 8.5 for d in self.human_sounds_traj[hs][:5]
] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
'''
def compute_s_sound(sound):
s1 = self.vowels[sound[0]]
s2 = [(self.vowels[sound[0]][0] + self.vowels[sound[1]][0]) / 2., (self.vowels[sound[0]][1] + self.vowels[sound[1]][1]) / 2.]
s3 = self.vowels[sound[1]]
s4 = [(self.vowels[sound[1]][0] + self.vowels[sound[2]][0]) / 2., (self.vowels[sound[1]][1] + self.vowels[sound[2]][1]) / 2.]
s5 = self.vowels[sound[2]]
rdm = 0.0 * (2.*np.random.random((1,10))[0] - 1.)
return list(rdm + np.array([f[0] for f in [s1, s2, s3, s4, s5]] + [f[1] for f in [s1, s2, s3, s4, s5]]))
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compute_s_sound(hs)
self.human_sounds_traj_std[hs] = [d - 8.5 for d in self.human_sounds_traj[hs][:5]] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
'''
self.diva_traj = None
self.produced_sound = None
self.sound_tol = 0.4
self.audio = audio
self.time_arm = 0.
self.time_diva = 0.
self.time_arm_per_it = 0.
self.time_diva_per_it = 0.
self.n_dmps = 7 #Quick and Dirty, todo json
self.timesteps = 50 #Quick and Dirty, todo json
# DIVA CONFIG
diva_cfg = dict(
m_mins=np.array([-1, -1, -1, -1, -1, -1, -1]),
m_maxs=np.array([1, 1, 1, 1, 1, 1, 1]),
s_mins=np.array([7.5, 9.25]),
s_maxs=np.array([9.5, 11.25]),
m_used=range(7),
s_used=range(1, 3),
rest_position_diva=list([0] * 7),
audio=self.audio,
diva_use_initial=True,
diva_use_goal=True,
used_diva=list([True] * 7),
n_dmps_diva=self.n_dmps,
n_bfs_diva=2,
move_steps=self.timesteps,
)
# Init Diva
self.diva = Diva(**diva_cfg)
def give_label(self, toy):
if toy == "culbuto_1":
#print "Caregiver says", self.human_sounds[0]
return self.human_sounds[0], self.human_sounds_traj_std[
self.human_sounds[0]]
elif toy == "distractor":
sound_id = np.random.choice([1, 2, 3])
#print "Caregiver says", self.human_sounds[sound_id]
return self.human_sounds[sound_id], self.human_sounds_traj_std[
self.human_sounds[sound_id]]
else:
raise NotImplementedError
def analysis_sound(self, diva_traj):
#return self.human_sounds[2]
#print self.human_sounds_traj
for hs in self.human_sounds:
error = np.linalg.norm(
np.array(self.human_sounds_traj[hs]) -
np.array([f[0] for f in diva_traj[[0, 12, 24, 37, 49]]] +
[f[1] for f in diva_traj[[0, 12, 24, 37, 49]]]))
if error < self.best_vocal_errors[hs]:
self.best_vocal_errors[hs] = error
if error < self.sound_tol:
print "***********Agent says", hs
return hs
return None
def get_caregiver_answer(self, touched_toy):
if self.pc:
if touched_toy:
return "contingent"
else:
return "distractor"
pa, k, theta, l = self.pa, 5., 0.25, np.log(2.) / 2.
def distractor_answer(l):
return np.random.exponential(1. / l)
def caregiver_answer(pa, k, theta):
if not touched_toy or np.random.random() < 1 - pa:
return None
else:
return np.random.gamma(k, theta)
c = caregiver_answer(pa, k, theta)
d = distractor_answer(l)
if c is not None and c < d and c < self.tau:
#print "contingent answer"
return "contingent"
elif d < self.tau:
# if touched_toy:
# print "distractor answer"
return "distractor"
# else:
# if touched_toy:
# print "no answer"
def baxter_analyse(self, is_culbuto_touched):
# CAREGIVER VOCAL REACTION
caregiver_answer = self.get_caregiver_answer(is_culbuto_touched)
#print caregiver_answer
sound_id = None
if caregiver_answer == "contingent":
if is_culbuto_touched:
rospy.logwarn("CULBUTO WAS TOUCHED HIP HIP HIP HOURRRAY")
sound_id, self.caregiver_sound = self.give_label("culbuto_1")
elif caregiver_answer == "distractor":
sound_id, self.caregiver_sound = self.give_label("distractor")
else:
# No sound
self.caregiver_sound = None
if not sound_id == None: # A word was choosen by caregiver
rospy.logwarn('Voice Node: Baxter says %s' % sound_id)
if self.audio: # Play the word
url = "http://raspberrypi.local:8080/api/v1/" + sound_id
contents = urllib2.urlopen(url).read()
#self.diva.compute_sensory_effect(self.full_human_motor_traj[sound_id],sound_power=30.)
return self.caregiver_sound
# Generate and play sound from diva trajectory
# Returns a 10-D sound trajectory for baxter and torso
# And a boolean set to True if the sound was culbuto's name
def execute_analyse(self, diva_traj):
t = time.time()
#Reshape trajectory list into matrix
diva_traj = np.reshape(diva_traj, (self.timesteps, self.n_dmps))
is_culbuto_name = False
self.raw_produced_sound = self.diva.compute_sensory_effect(diva_traj)
self.produced_sound = self.analysis_sound(self.raw_produced_sound)
if self.produced_sound is not None:
# parent gives object if label is produced and object out of reach
if self.produced_sound == self.human_sounds[0]:
is_culbuto_name = True
rospy.logwarn(
"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!CULBUTO NAME WAS PRONOUNCED"
)
self.self_sound = [
f for formants in self.raw_produced_sound[[0, 12, 24, 37, 49]]
for f in formants
]
self.self_sound = self.self_sound[0::2] + self.self_sound[1::2]
# MAP TO STD INTERVAL (only extracts first 2 formants)
self.downsampled_self_sound = [
d - 8.5 for d in self.self_sound[:5]
] + [d - 10.25 for d in self.self_sound[5:]]
#self.downsampled_caregiver_sound = [d - 8.5 for d in self.caregiver_sound[:5]] + [d - 10.25 for d in self.caregiver_sound[5:]]
#return bounds_min_max(s, self.conf.s_mins, self.conf.s_maxs)
flat_raw_produced_sound = [
f for formants in self.raw_produced_sound for f in formants
]
return self.downsampled_self_sound, None, is_culbuto_name, self.produced_sound, flat_raw_produced_sound
class RecordActionServer(object):
# create messages that are used to publish feedback/result
_feedback = RecordFeedback()
_result = RecordResult()
def __init__(self):
self._action_name = '/pobax_playground/perception/record_server'
self._as = actionlib.SimpleActionServer(self._action_name,
RecordAction,
execute_cb=self.execute_cb,
auto_start=False)
self._as.start()
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'perception.json')) as f:
self.params = json.load(f)
self.rate = rospy.Rate(self.params['recording_rate'])
self.topics = TopicAggregator()
def get(self):
#TODO ADD VOICE
state = SensorialState(hand=self.topics.torso_l_eef,
culbuto_1=self.topics.culbuto_1)
return state
# Choose nb_points from sensorial_demo, evenly spaced
# (except for last point which is always the last one recorded)
def choose_points(self, sensorial_traj, nb_points):
sequence_size = len(sensorial_traj.points)
if sequence_size < nb_points:
rospy.logerr(
'%s: Recording time was not long enough to gather enough data points: recorded: %s vs asked: %s'
% (self._action_name, sequence_size, nb_points))
return
# Handy fonction for choosing m evenly spaced elements from a sequence of length n
f = lambda m, n: [i * n // m + n // (2 * m) for i in range(m)]
choosen_idx = f(nb_points, sequence_size)
# Change the last point in choosen sequence by the last recorder point
choosen_idx[-1] = (sequence_size - 1)
result = SensorialTrajectory()
for i in choosen_idx:
result.points.append(sensorial_traj.points[i])
return result
def execute_cb(self, goal):
rospy.loginfo('%s: Start non-blocking recording of culbuto' %
(self._action_name))
sensorial_demo = SensorialTrajectory()
while not rospy.is_shutdown():
if self._as.is_preempt_requested(
): # When requested, we stop registering positions
rospy.loginfo('%s: Preempted' % self._action_name)
break
sensorial_demo.points.append(self.get())
self.rate.sleep()
self._result.sensorial_trajectory = self.choose_points(
sensorial_demo, goal.nb_points.data)
rospy.loginfo('%s: Succeeded' % self._action_name)
self._as.set_succeeded(self._result)
def __init__(self, gui=False, audio=False):
self.t = 0
# ARM CONFIG
arm_cfg = dict(m_mins=[-1.] * 3 * 7,
m_maxs=[1.] * 3 * 7,
s_mins=[-1.] * 3 * 50,
s_maxs=[1.] * 3 * 50,
lengths=[0.5, 0.3, 0.2],
angle_shift=0.5,
rest_state=[0., 0., 0.],
n_dmps=3,
n_bfs=6,
timesteps=50,
gui=gui)
# SOUND CONFIG
self.v_o = list(np.log2([500, 900]))
self.v_y = list(np.log2([300, 1700]))
self.v_u = list(np.log2([300, 800]))
self.v_e = list(np.log2([400, 2200]))
self.v_i = list(np.log2([300, 2300]))
self.vowels = dict(o=self.v_o,
y=self.v_y,
u=self.v_u,
e=self.v_e,
i=self.v_i)
# Retrieve caregiver sounds and trajectories from json
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'human_sounds.pickle')) as f:
self.full_human_motor_traj, self.full_human_sounds_traj = pickle.load(
f)
self.human_sounds = self.full_human_sounds_traj.keys()
rospy.loginfo('Voice node using the word %s for culbuto name' %
self.human_sounds[0])
def compress_sound_traj(sound):
assert (len(sound) == 100)
f1s = sound[:50]
f3s = sound[50:]
return np.append(f1s[np.array([0, 12, 24, 37, 49])],
f3s[np.array([0, 12, 24, 37, 49])])
#reduce number of sounds
self.human_sounds = ['eyu', 'oey', 'eou', 'oyi']
#random.shuffle(self.human_sounds)
print self.human_sounds
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compress_sound_traj(
self.full_human_sounds_traj[hs])
self.human_sounds_traj_std[hs] = [
d - 8.5 for d in self.human_sounds_traj[hs][:5]
] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
self.sound_tol = 0.4
# DIVA CONFIG
diva_cfg = dict(
m_mins=np.array([-1, -1, -1, -1, -1, -1, -1]),
m_maxs=np.array([1, 1, 1, 1, 1, 1, 1]),
s_mins=np.array([7.5, 9.25]),
s_maxs=np.array([9.5, 11.25]),
m_used=range(7),
s_used=range(1, 3),
rest_position_diva=list([0] * 7),
audio=audio,
diva_use_initial=True,
diva_use_goal=True,
used_diva=list([True] * 7),
n_dmps_diva=7,
n_bfs_diva=2,
move_steps=50,
)
self.arm = ArmEnvironment(**arm_cfg)
self.diva = DivaEnvironment(**diva_cfg)
self.timesteps = 50
# OBJECTS CONFIG
self.caregiver_gives_obj_factor = 0.01
self.tool_length = 0.5
self.handle_tol = 0.2
self.handle_tol_sq = self.handle_tol * self.handle_tol
self.handle_noise = 0.
self.object_tol_hand = 0.2
self.object_tol_hand_sq = self.object_tol_hand * self.object_tol_hand
self.object_tol_tool = 0.2
self.object_tol_tool_sq = self.object_tol_tool * self.object_tol_tool
self.diva_traj = None
self.produced_sound = None
Environment.__init__(self,
m_mins=[-1.] * (21 + 28),
m_maxs=[1.] * (21 + 28),
s_mins=[-1.] * 56,
s_maxs=[1.] * 56)
self.current_tool = [-0.5, 0., 0.5, 0.]
self.current_toy1 = [0.5, 0.5, 0.]
#self.current_toy2 = [0.7, 0.7, 0.]
#self.current_toy3 = [0.9, 0.9, 0.]
self.current_caregiver = [0., 1.7]
self.reset()
self.compute_tool()
self.purge_logs()
if self.arm.gui:
self.init_plot()
self.count_diva = 0
self.count_arm = 0
self.count_tool = 0
self.count_toy1_by_tool = 0
#self.count_toy2_by_tool = 0
#self.count_toy3_by_tool = 0
self.count_toy1_by_hand = 0
#self.count_toy2_by_hand = 0
#self.count_toy3_by_hand = 0
self.count_parent_give_label = 0
self.count_parent_give_object = 0
self.count_produced_sounds = {}
for hs in self.human_sounds:
self.count_produced_sounds[hs] = 0
self.time_arm = 0.
self.time_diva = 0.
self.time_arm_per_it = 0.
self.time_diva_per_it = 0.
class LearningNode(object):
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'learning.json')) as f:
self.params = json.load(f)
#with open(join(self.rospack.get_path('pobax_playground'), 'config', 'dmps.json')) as f:
# self.dmps_params = json.load(f)
self.arm_translator = EnvironmentTranslatorArm()
self.diva_translator = EnvironmentTranslatorDiva()
self.config = dict(m_mins=[-1.] * 68,
m_maxs=[1.] * 68,
s_mins=[-1.] * self.params["sensory_state_size"],
s_maxs=[1.] * self.params["sensory_state_size"])
self.learning = Learning(
self.config,
sensory_state_size=self.params["sensory_state_size"],
model_babbling=self.params["model_babbling"],
n_motor_babbling=self.params["n_motor_babbling"],
explo_noise=self.params["explo_noise"],
choice_eps=self.params["choice_eps"],
proba_imitate=self.params["proba_imitate"],
tau=self.params["tau"])
self.experiment_name = rospy.get_param(
"/pobax_playground/experiment_name", "experiment")
rospy.loginfo("Learning node will write {}".format(
self.experiment_name))
# Saved experiment files
self.dir = join(self.rospack.get_path('pobax_playground'), 'logs')
if not os.path.isdir(self.dir):
os.makedirs(self.dir)
self.experiment_file = join(self.dir, self.experiment_name + '.pickle')
starting_iteration = 0
if isfile(self.experiment_file):
starting_iteration = self.learning.restart_from_end_of_file(
self.experiment_file)
else:
self.learning.start()
rospy.set_param("/pobax_playground/starting_iteration",
starting_iteration)
# Serving these services
self.service_name_perceive = "/pobax_playground/learning/perceive"
self.service_name_produce = "/pobax_playground/learning/produce"
self.service_name_save = "/pobax_playground/learning/save"
# Publishing these topics
#self.pub_interests = rospy.Publisher('/pobax_playground/learning/interests', Interests, queue_size=1, latch=True)
self.pub_focus = rospy.Publisher(
'/pobax_playground/learning/current_focus',
String,
queue_size=1,
latch=True)
# Using these services
self.service_name_get_perception = "/pobax_playground/perception/get"
for service in [self.service_name_get_perception]:
rospy.loginfo(
"Learning node is waiting service {}...".format(service))
rospy.wait_for_service(service)
self.get_state = rospy.ServiceProxy(self.service_name_get_perception,
GetSensorialState)
def run(self):
rospy.Service(self.service_name_perceive, Perceive, self.cb_perceive)
rospy.Service(self.service_name_produce, Produce, self.cb_produce)
rospy.Service(self.service_name_save, Save, self.cb_save)
rospy.loginfo("Learning is up!")
rate = rospy.Rate(self.params['publish_rate'])
while not rospy.is_shutdown():
#self.publish()
rate.sleep()
'''
def publish(self):
interests_array = self.learning.get_normalized_interests_evolution()
interests = Interests()
interests.names = self.learning.get_space_names()
interests.num_iterations = UInt32(len(interests_array))
interests.interests = [Float32(val) for val in interests_array.flatten()]
self.pub_interests.publish(interests)
'''
################################# Service callbacks
def cb_perceive(self, request):
#rospy.logwarn("Aborting perception, TODO modify learning core for new sensory space")
#return PerceiveResponse()
s_physical = self.arm_translator.sensory_trajectory_msg_to_list(
request)
s_sound = self.diva_translator.sensory_trajectory_msg_to_list(request)
rospy.loginfo("Learning node is perceiving sensory trajectory")
#print "physical"
#print s_physical
#print "sound"
#print s_sound
success = self.learning.perceive(np.append(s_physical, s_sound))
if not success:
rospy.logerr("Learner could not perceive this trajectory")
return PerceiveResponse()
def cb_produce(self, request):
rospy.loginfo("Learning node is requesting the current state")
state = self.get_state(GetSensorialStateRequest()).state
rospy.loginfo("Learning node is producing...")
w, param_type = self.learning.produce(
self.arm_translator.get_context(state))
produce_msg = ProduceResponse(trajectory_type=param_type)
if param_type == "arm":
trajectory_matrix = self.arm_translator.w_to_trajectory(w)
trajectory_msg = self.arm_translator.matrix_to_trajectory_msg(
trajectory_matrix)
produce_msg.torso_trajectory = trajectory_msg
elif param_type == "diva":
trajectory_matrix = self.diva_translator.w_to_trajectory(w)
trajectory_msg = self.diva_translator.matrix_to_trajectory_msg(
trajectory_matrix)
produce_msg.vocal_trajectory = trajectory_msg
else:
rospy.logerr(
"Learning Node received an unknown param_type when calling produce()"
)
self.ready_for_interaction = True
return produce_msg
def cb_save(self, request):
rospy.loginfo("Learning node saving current state...")
data = self.learning.save()
with open(self.experiment_file, 'w') as f:
pickle.dump(data, f)
rospy.loginfo("Saved file (periodic save) into {}".format(
self.experiment_file))
return SaveResponse()
def __init__(self, tau, pa, pc, gui=False, audio=False):
self.t = 0
self.tau = tau
self.pa = pa
self.pc = pc
# SOUND CONFIG
self.v_o = list(np.log2([500, 900]))
self.v_y = list(np.log2([300, 1700]))
self.v_u = list(np.log2([300, 800]))
self.v_e = list(np.log2([400, 2200]))
self.v_i = list(np.log2([300, 2300]))
# Retrieve caregiver sounds and trajectories from json
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'human_sounds.pickle')) as f:
self.full_human_motor_traj, self.full_human_sounds_traj = pickle.load(
f)
self.human_sounds = self.full_human_sounds_traj.keys()
rospy.loginfo('Voice node using the word %s for culbuto name' %
self.human_sounds[0])
self.vowels = dict(o=self.v_o,
y=self.v_y,
u=self.v_u,
e=self.v_e,
i=self.v_i)
self.human_sounds = ['eyu', 'oey', 'eou', 'oyi']
#random.shuffle(self.human_sounds)
print "human sounds", self.human_sounds
def compress_sound_traj(sound):
assert (len(sound) == 100)
f1s = sound[:50]
f3s = sound[50:]
return np.append(f1s[np.array([0, 12, 24, 37, 49])],
f3s[np.array([0, 12, 24, 37, 49])])
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compress_sound_traj(
self.full_human_sounds_traj[hs])
self.human_sounds_traj_std[hs] = [
d - 8.5 for d in self.human_sounds_traj[hs][:5]
] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
'''
def compute_s_sound(sound):
s1 = self.vowels[sound[0]]
s2 = [(self.vowels[sound[0]][0] + self.vowels[sound[1]][0]) / 2., (self.vowels[sound[0]][1] + self.vowels[sound[1]][1]) / 2.]
s3 = self.vowels[sound[1]]
s4 = [(self.vowels[sound[1]][0] + self.vowels[sound[2]][0]) / 2., (self.vowels[sound[1]][1] + self.vowels[sound[2]][1]) / 2.]
s5 = self.vowels[sound[2]]
rdm = 0.0 * (2.*np.random.random((1,10))[0] - 1.)
return list(rdm + np.array([f[0] for f in [s1, s2, s3, s4, s5]] + [f[1] for f in [s1, s2, s3, s4, s5]]))
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compute_s_sound(hs)
self.human_sounds_traj_std[hs] = [d - 8.5 for d in self.human_sounds_traj[hs][:5]] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
'''
self.diva_traj = None
self.produced_sound = None
self.sound_tol = 0.4
self.audio = audio
self.time_arm = 0.
self.time_diva = 0.
self.time_arm_per_it = 0.
self.time_diva_per_it = 0.
self.n_dmps = 7 #Quick and Dirty, todo json
self.timesteps = 50 #Quick and Dirty, todo json
# DIVA CONFIG
diva_cfg = dict(
m_mins=np.array([-1, -1, -1, -1, -1, -1, -1]),
m_maxs=np.array([1, 1, 1, 1, 1, 1, 1]),
s_mins=np.array([7.5, 9.25]),
s_maxs=np.array([9.5, 11.25]),
m_used=range(7),
s_used=range(1, 3),
rest_position_diva=list([0] * 7),
audio=self.audio,
diva_use_initial=True,
diva_use_goal=True,
used_diva=list([True] * 7),
n_dmps_diva=self.n_dmps,
n_bfs_diva=2,
move_steps=self.timesteps,
)
# Init Diva
self.diva = Diva(**diva_cfg)
def __init__(self):
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'learning.json')) as f:
self.params = json.load(f)
#with open(join(self.rospack.get_path('pobax_playground'), 'config', 'dmps.json')) as f:
# self.dmps_params = json.load(f)
self.arm_translator = EnvironmentTranslatorArm()
self.diva_translator = EnvironmentTranslatorDiva()
self.config = dict(m_mins=[-1.] * 68,
m_maxs=[1.] * 68,
s_mins=[-1.] * self.params["sensory_state_size"],
s_maxs=[1.] * self.params["sensory_state_size"])
self.learning = Learning(
self.config,
sensory_state_size=self.params["sensory_state_size"],
model_babbling=self.params["model_babbling"],
n_motor_babbling=self.params["n_motor_babbling"],
explo_noise=self.params["explo_noise"],
choice_eps=self.params["choice_eps"],
proba_imitate=self.params["proba_imitate"],
tau=self.params["tau"])
self.experiment_name = rospy.get_param(
"/pobax_playground/experiment_name", "experiment")
rospy.loginfo("Learning node will write {}".format(
self.experiment_name))
# Saved experiment files
self.dir = join(self.rospack.get_path('pobax_playground'), 'logs')
if not os.path.isdir(self.dir):
os.makedirs(self.dir)
self.experiment_file = join(self.dir, self.experiment_name + '.pickle')
starting_iteration = 0
if isfile(self.experiment_file):
starting_iteration = self.learning.restart_from_end_of_file(
self.experiment_file)
else:
self.learning.start()
rospy.set_param("/pobax_playground/starting_iteration",
starting_iteration)
# Serving these services
self.service_name_perceive = "/pobax_playground/learning/perceive"
self.service_name_produce = "/pobax_playground/learning/produce"
self.service_name_save = "/pobax_playground/learning/save"
# Publishing these topics
#self.pub_interests = rospy.Publisher('/pobax_playground/learning/interests', Interests, queue_size=1, latch=True)
self.pub_focus = rospy.Publisher(
'/pobax_playground/learning/current_focus',
String,
queue_size=1,
latch=True)
# Using these services
self.service_name_get_perception = "/pobax_playground/perception/get"
for service in [self.service_name_get_perception]:
rospy.loginfo(
"Learning node is waiting service {}...".format(service))
rospy.wait_for_service(service)
self.get_state = rospy.ServiceProxy(self.service_name_get_perception,
GetSensorialState)
class CogSci2017Environment(Environment):
def __init__(self, gui=False, audio=False):
self.t = 0
# ARM CONFIG
arm_cfg = dict(m_mins=[-1.] * 3 * 7,
m_maxs=[1.] * 3 * 7,
s_mins=[-1.] * 3 * 50,
s_maxs=[1.] * 3 * 50,
lengths=[0.5, 0.3, 0.2],
angle_shift=0.5,
rest_state=[0., 0., 0.],
n_dmps=3,
n_bfs=6,
timesteps=50,
gui=gui)
# SOUND CONFIG
self.v_o = list(np.log2([500, 900]))
self.v_y = list(np.log2([300, 1700]))
self.v_u = list(np.log2([300, 800]))
self.v_e = list(np.log2([400, 2200]))
self.v_i = list(np.log2([300, 2300]))
self.vowels = dict(o=self.v_o,
y=self.v_y,
u=self.v_u,
e=self.v_e,
i=self.v_i)
# Retrieve caregiver sounds and trajectories from json
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'human_sounds.pickle')) as f:
self.full_human_motor_traj, self.full_human_sounds_traj = pickle.load(
f)
self.human_sounds = self.full_human_sounds_traj.keys()
rospy.loginfo('Voice node using the word %s for culbuto name' %
self.human_sounds[0])
def compress_sound_traj(sound):
assert (len(sound) == 100)
f1s = sound[:50]
f3s = sound[50:]
return np.append(f1s[np.array([0, 12, 24, 37, 49])],
f3s[np.array([0, 12, 24, 37, 49])])
#reduce number of sounds
self.human_sounds = ['eyu', 'oey', 'eou', 'oyi']
#random.shuffle(self.human_sounds)
print self.human_sounds
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compress_sound_traj(
self.full_human_sounds_traj[hs])
self.human_sounds_traj_std[hs] = [
d - 8.5 for d in self.human_sounds_traj[hs][:5]
] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
self.sound_tol = 0.4
# DIVA CONFIG
diva_cfg = dict(
m_mins=np.array([-1, -1, -1, -1, -1, -1, -1]),
m_maxs=np.array([1, 1, 1, 1, 1, 1, 1]),
s_mins=np.array([7.5, 9.25]),
s_maxs=np.array([9.5, 11.25]),
m_used=range(7),
s_used=range(1, 3),
rest_position_diva=list([0] * 7),
audio=audio,
diva_use_initial=True,
diva_use_goal=True,
used_diva=list([True] * 7),
n_dmps_diva=7,
n_bfs_diva=2,
move_steps=50,
)
self.arm = ArmEnvironment(**arm_cfg)
self.diva = DivaEnvironment(**diva_cfg)
self.timesteps = 50
# OBJECTS CONFIG
self.caregiver_gives_obj_factor = 0.01
self.tool_length = 0.5
self.handle_tol = 0.2
self.handle_tol_sq = self.handle_tol * self.handle_tol
self.handle_noise = 0.
self.object_tol_hand = 0.2
self.object_tol_hand_sq = self.object_tol_hand * self.object_tol_hand
self.object_tol_tool = 0.2
self.object_tol_tool_sq = self.object_tol_tool * self.object_tol_tool
self.diva_traj = None
self.produced_sound = None
Environment.__init__(self,
m_mins=[-1.] * (21 + 28),
m_maxs=[1.] * (21 + 28),
s_mins=[-1.] * 56,
s_maxs=[1.] * 56)
self.current_tool = [-0.5, 0., 0.5, 0.]
self.current_toy1 = [0.5, 0.5, 0.]
#self.current_toy2 = [0.7, 0.7, 0.]
#self.current_toy3 = [0.9, 0.9, 0.]
self.current_caregiver = [0., 1.7]
self.reset()
self.compute_tool()
self.purge_logs()
if self.arm.gui:
self.init_plot()
self.count_diva = 0
self.count_arm = 0
self.count_tool = 0
self.count_toy1_by_tool = 0
#self.count_toy2_by_tool = 0
#self.count_toy3_by_tool = 0
self.count_toy1_by_hand = 0
#self.count_toy2_by_hand = 0
#self.count_toy3_by_hand = 0
self.count_parent_give_label = 0
self.count_parent_give_object = 0
self.count_produced_sounds = {}
for hs in self.human_sounds:
self.count_produced_sounds[hs] = 0
self.time_arm = 0.
self.time_diva = 0.
self.time_arm_per_it = 0.
self.time_diva_per_it = 0.
def save(self):
return dict(
t=self.t,
human_sounds=self.human_sounds,
best_vocal_errors=self.best_vocal_errors,
best_vocal_errors_evolution=self.best_vocal_errors_evolution,
count_diva=self.count_diva,
count_arm=self.count_arm,
count_tool=self.count_tool,
count_toy1_by_tool=self.count_toy1_by_tool,
#count_toy2_by_tool=self.count_toy2_by_tool,
#count_toy3_by_tool=self.count_toy3_by_tool,
count_toy1_by_hand=self.count_toy1_by_hand,
#count_toy2_by_hand=self.count_toy2_by_hand,
#count_toy3_by_hand=self.count_toy3_by_hand,
count_parent_give_label=self.count_parent_give_label,
count_parent_give_object=self.count_parent_give_object,
count_produced_sounds=self.count_produced_sounds,
)
def reset(self):
if self.t % 20 == 0:
self.reset_toys()
self.current_tool[3] = 0.
self.current_toy1[2] = 0.
#self.current_toy2[2] = 0.
#self.current_toy3[2] = 0.
self.reset_caregiver()
self.current_context = self.get_current_context()
self.hand = []
self.tool = []
self.toy1 = []
#self.toy2 = []
#self.toy3 = []
self.caregiver = []
def purge_logs(self):
self.logs_tool = []
self.logs_toy1 = []
#self.logs_toy2 = []
#self.logs_toy3 = []
self.logs_caregiver = []
def reset_tool(self):
self.current_tool[:2] = self.reset_rand2d(region=1)
self.current_tool[2] = np.random.random()
self.compute_tool()
def set_tool(self, pos, angle):
self.current_tool[:2] = pos
self.current_tool[2] = angle
self.compute_tool()
def reset_toys(self, region=0):
self.current_toy1[:2] = self.reset_rand2d(region=region)
#self.current_toy2[:2] = self.reset_rand2d(region=region)
#self.current_toy3[:2] = self.reset_rand2d(region=region)
def set_toys(self, pos1, pos2, pos3):
self.current_toy1[:2] = pos1
#self.current_toy2[:2] = pos2
#self.current_toy3[:2] = pos3
def reset_caregiver(self):
self.current_caregiver = self.reset_rand2d()
def set_caregiver(self, pos):
self.current_caregiver = pos
def reset_rand2d(self, region=None):
if region == 0:
rdm = np.random.random()
if rdm < 1. / 3.:
return self.reset_rand2d(region=1)
elif rdm < 2. / 3.:
return self.reset_rand2d(region=2)
else:
return self.reset_rand2d(region=3)
elif region == 1:
alpha = 2. * np.pi * np.random.random()
r = np.random.random()
return [r * np.cos(alpha), r * np.sin(alpha)]
elif region == 2:
alpha = 2. * np.pi * np.random.random()
r = 1. + 0.5 * np.random.random()
return [r * np.cos(alpha), r * np.sin(alpha)]
elif region == 3:
alpha = 2. * np.pi * np.random.random()
r = 1.5 + 0.5 * np.random.random()
return [r * np.cos(alpha), r * np.sin(alpha)]
elif region is None:
return [4. * np.random.random() - 2., 4. * np.random.random() - 2.]
def get_current_context(self):
return [
d / 2. for d in self.current_tool[:2] + self.current_toy1[:2] +
self.current_caregiver
]
def compute_tool(self):
a = np.pi * self.current_tool[2]
self.tool_end_pos = [
self.current_tool[0] + np.cos(a) * self.tool_length,
self.current_tool[1] + np.sin(a) * self.tool_length
]
def compute_motor_command(self, m_ag):
return bounds_min_max(m_ag, self.conf.m_mins, self.conf.m_maxs)
def motor_babbling(self, arm=False, audio=False):
m = rand_bounds(self.conf.m_bounds)[0]
if arm:
r = 1.
elif audio:
r = 0.
else:
r = np.random.random()
if r < 0.5:
m[:21] = 0.
else:
m[21:] = 0.
return m
def is_hand_free(self):
return self.current_tool[3] == 0 and (not self.current_toy1[2] == 1)
def is_tool_free(self):
return (not self.current_toy1[2] == 2)
def give_label(self, toy):
if toy == "toy1":
#print "Caregiver says", self.human_sounds[0]
return self.human_sounds_traj[self.human_sounds[0]]
elif toy == "random":
sound_id = np.random.choice([1, 2, 3])
#print "Caregiver says", self.human_sounds[sound_id]
return self.human_sounds_traj[self.human_sounds[sound_id]]
else:
raise NotImplementedError
def analysis_sound(self, diva_traj):
#return self.human_sounds[2]
best = None
is_best_found = False
best_best = None
best_best_sound_tol = 999
for hs in self.human_sounds:
error = np.linalg.norm(
np.array(self.human_sounds_traj[hs]) -
np.array([f[0] for f in diva_traj[[0, 12, 24, 37, 49]]] +
[f[1] for f in diva_traj[[0, 12, 24, 37, 49]]]))
if error < self.best_vocal_errors[hs]:
self.best_vocal_errors[hs] = error
if error < self.sound_tol:
if not is_best_found:
best = hs
best_found = True
if error < best_best_sound_tol:
best_best = hs
best_best_sound_tol = error
if best:
if not best_best == best:
print "########### I WAS RIGHT #########, best is " + best + "but best best is " + best_best
print "***********Agent says", best
return best
else:
return None
def caregiver_moves_obj(self, caregiver_pos, current_toy):
middle = [caregiver_pos[0] / 2, caregiver_pos[1] / 2]
return [
current_toy[0] + self.caregiver_gives_obj_factor *
(middle[0] - current_toy[0]), current_toy[1] +
self.caregiver_gives_obj_factor * (middle[1] - current_toy[1]),
current_toy[2]
]
def compute_sensori_effect(self, m):
t = time.time()
m_arm = m[:21]
m_diva = m[21:]
assert np.linalg.norm(m_arm) * np.linalg.norm(m_diva) == 0.
if np.linalg.norm(m_arm) > 0.:
cmd = "arm"
else:
cmd = "diva"
self.purge_logs()
arm_traj = self.arm.update(m_arm)
#print "arm traj", arm_traj
if cmd == "diva":
diva_traj = self.diva.update(m_diva)
self.diva_traj = diva_traj
self.produced_sound = self.analysis_sound(self.diva_traj)
if self.produced_sound is not None:
self.count_produced_sounds[self.produced_sound] += 1
if self.produced_sound in self.human_sounds[:3]:
self.count_parent_give_object += 1
else:
diva_traj = np.zeros((50, 2))
self.produced_sound = None
for i in range(self.timesteps):
# Arm
arm_x, arm_y, arm_angle = arm_traj[i]
# Tool
if not self.current_tool[3]:
if self.is_hand_free() and (
(arm_x - self.current_tool[0])**2. +
(arm_y - self.current_tool[1])**2. < self.handle_tol_sq):
self.current_tool[0] = arm_x
self.current_tool[1] = arm_y
self.current_tool[2] = np.mod(
arm_angle + self.handle_noise * np.random.randn() + 1,
2) - 1
self.compute_tool()
self.current_tool[3] = 1
else:
self.current_tool[0] = arm_x
self.current_tool[1] = arm_y
self.current_tool[2] = np.mod(
arm_angle + self.handle_noise * np.random.randn() + 1,
2) - 1
self.compute_tool()
self.logs_tool.append([
self.current_tool[:2], self.current_tool[2], self.tool_end_pos,
self.current_tool[3]
])
if cmd == "arm":
# Toy 1
if self.current_toy1[2] == 1 or (self.is_hand_free() and (
(arm_x - self.current_toy1[0])**2 +
(arm_y - self.current_toy1[1])**2 <
self.object_tol_hand_sq)):
self.current_toy1[0] = arm_x
self.current_toy1[1] = arm_y
self.current_toy1[2] = 1
if self.current_toy1[2] == 2 or (
(not self.current_toy1[2] == 1) and self.is_tool_free() and
((self.tool_end_pos[0] - self.current_toy1[0])**2 +
(self.tool_end_pos[1] - self.current_toy1[1])**2 <
self.object_tol_tool_sq)):
self.current_toy1[0] = self.tool_end_pos[0]
self.current_toy1[1] = self.tool_end_pos[1]
self.current_toy1[2] = 2
self.logs_toy1.append([self.current_toy1])
self.logs_caregiver.append([self.current_caregiver])
else:
# parent gives object if label is produced
if self.produced_sound == self.human_sounds[0]:
self.current_toy1 = self.caregiver_moves_obj(
self.current_caregiver, self.current_toy1)
self.logs_toy1.append([self.current_toy1])
self.logs_caregiver.append([self.current_caregiver])
if i in [0, 12, 24, 37, 49]:
self.hand = self.hand + [arm_x, arm_y]
self.tool = self.tool + self.current_tool[:2]
self.toy1 = self.toy1 + self.current_toy1[:2]
self.caregiver = self.caregiver + self.current_caregiver
if self.arm.gui:
if i % 5 == 0:
self.plot_step(self.ax, i)
if cmd == "arm":
# parent gives label if object is touched by hand
if self.current_toy1[2] == 1:
label = self.give_label("toy1")
else:
label = self.give_label("random")
self.sound = label
#print "parent sound", label, self.sound
else:
self.sound = [
f for formants in diva_traj[[0, 12, 24, 37, 49]]
for f in formants
]
self.sound = self.sound[0::2] + self.sound[1::2]
# Sort dims
self.hand = self.hand[0::2] + self.hand[1::2]
self.tool = self.tool[0::2] + self.tool[1::2]
self.toy1 = self.toy1[0::2] + self.toy1[1::2]
self.caregiver = self.caregiver[0::2] + self.caregiver[1::2]
#self.sound = self.sound[0::2] + self.sound[1::2]
# Analysis
if np.linalg.norm(m[21:]) > 0:
self.count_diva += 1
else:
self.count_arm += 1
if self.current_tool[3]:
self.count_tool += 1
if self.current_toy1[2] == 1:
self.count_toy1_by_hand += 1
elif self.current_tool[3] and self.current_toy1[2] == 2:
self.count_toy1_by_tool += 1
self.count_parent_give_label = self.count_toy1_by_hand
if cmd == "arm":
self.time_arm += time.time() - t
self.time_arm_per_it = self.time_arm / self.count_arm
else:
self.time_diva += time.time() - t
self.time_diva_per_it = self.time_diva / self.count_diva
#print "previous context", len(self.current_context), self.current_context
#print "s_hand", len(self.hand), self.hand
#print "s_tool", len(self.tool), self.tool
#print "s_toy1", len(self.toy1), self.toy1
#print "s_toy2", len(self.toy2), self.toy2
#print "s_toy3", len(self.toy3), self.toy3
#print "s_sound", len(self.sound), self.sound
#print "s_caregiver", len(self.caregiver), self.caregiver
self.t += 1
if self.t % 100 == 0:
self.best_vocal_errors_evolution += [self.best_vocal_errors.copy()]
if self.t % 1000 == 0:
print "best_vocal_errors", [(hs, self.best_vocal_errors[hs])
for hs in self.human_sounds]
context = self.current_context
# MAP TO STD INTERVAL
hand = [d / 2 for d in self.hand]
tool = [d / 2 for d in self.tool]
toy1 = [d / 2 for d in self.toy1]
sound = [d - 8.5 for d in self.sound[:5]
] + [d - 10.25 for d in self.sound[5:]]
caregiver = [d / 2 for d in self.caregiver]
s = context + hand + tool + toy1 + sound + caregiver
#print "s_sound", sound
return bounds_min_max(s, self.conf.s_mins, self.conf.s_maxs)
def update(self, m_ag, reset=True, log=True):
""" Computes sensorimotor values from motor orders.
:param numpy.array m_ag: a motor command with shape (self.conf.m_ndims, ) or a set of n motor commands of shape (n, self.conf.m_ndims)
:param bool log: emit the motor and sensory values for logging purpose (default: True).
:returns: an array of shape (self.conf.ndims, ) or (n, self.conf.ndims) according to the shape of the m_ag parameter, containing the motor values (which can be different from m_ag, e.g. bounded according to self.conf.m_bounds) and the corresponding sensory values.
.. note:: self.conf.ndims = self.conf.m_ndims + self.conf.s_ndims is the dimensionality of the sensorimotor space (dim of the motor space + dim of the sensory space).
"""
if len(np.array(m_ag).shape) == 1:
s = self.one_update(m_ag, log)
else:
s = []
for m in m_ag:
s.append(self.one_update(m, log))
s = np.array(s)
if reset:
self.reset()
return s
def print_stats(self):
print "\n----------------------\nEnvironment Statistics\n----------------------\n"
print "# Iterations:", self.t
print "# Arm trials:", self.count_arm
print "# Vocal trials:", self.count_diva
print "# Tool actions:", self.count_tool
print "# toy sounds:", self.human_sounds[0], self.human_sounds[
1], self.human_sounds[2]
print "# Produced sounds:", self.count_produced_sounds
print "# Toy1 was reached by tool:", self.count_toy1_by_tool
print "# Toy1 was reached by hand:", self.count_toy1_by_hand
print "# Parent gave vocal labels:", self.count_parent_give_label
print "# Parent gave object:", self.count_parent_give_object
print
def init_plot(self):
#plt.ion()
self.ax = plt.subplot()
plt.gcf().set_size_inches(6., 6., forward=True)
plt.gca().set_aspect('equal')
#plt.show(block=False)
plt.axis([-2, 2, -2, 2])
plt.draw()
def plot_tool_step(self, ax, i, **kwargs_plot):
handle_pos = self.logs_tool[i][0]
end_pos = self.logs_tool[i][2]
ax.plot([handle_pos[0], end_pos[0]], [handle_pos[1], end_pos[1]],
'-',
color=colors_config['stick'],
lw=6,
**kwargs_plot)
ax.plot(handle_pos[0],
handle_pos[1],
'o',
color=colors_config['gripper'],
ms=12,
**kwargs_plot)
ax.plot(end_pos[0],
end_pos[1],
'o',
color=colors_config['magnetic'],
ms=12,
**kwargs_plot)
def plot_toy1_step(self, ax, i, **kwargs_plot):
pos = self.logs_toy1[i][0]
rectangle = plt.Rectangle((pos[0] - 0.1, pos[1] - 0.1),
0.2,
0.2,
color=colors[3],
**kwargs_plot)
ax.add_patch(rectangle)
def plot_caregiver_step(self, ax, i, **kwargs_plot):
pos = self.logs_caregiver[i][0]
rectangle = plt.Rectangle((pos[0] - 0.1, pos[1] - 0.1),
0.2,
0.2,
color="black",
**kwargs_plot)
ax.add_patch(rectangle)
def plot_step(self, ax, i, clean=True, **kwargs_plot):
#t0 = time.time()
plt.pause(0.0001)
#print "t1", time.time() - t0
if clean:
plt.cla()
#print "t2", time.time() - t0
self.arm.plot_step(ax, i, **kwargs_plot)
self.plot_tool_step(ax, i, **kwargs_plot)
self.plot_toy1_step(ax, i, **kwargs_plot)
self.plot_caregiver_step(ax, i, **kwargs_plot)
#print "t3", time.time() - t0
if clean:
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.gca().set_xticklabels([])
plt.gca().set_yticklabels([])
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.xlabel("")
plt.ylabel("")
plt.draw()
plt.show(block=False)
