def build_keyframe_graph(self, demonstrations, bandwidth, vectorizor=None):
self.G = KeyframeGraph()
keyframe_clustering = KeyframeClustering()
"""
Generate clusters using labeled observations, build the models, graphs, and attributes for each
cluster in the KeyFrameGraph
"""
clusters = keyframe_clustering.get_clusters(demonstrations)
for cluster_id in clusters.keys():
self.G.add_node(cluster_id)
self.G.nodes[cluster_id]["observations"] = clusters[cluster_id][
"observations"]
self.G.nodes[cluster_id]["keyframe_type"] = clusters[cluster_id][
"keyframe_type"]
####################################################
# NEED TO BE ABLE TO SUPPORT CC-LFD eventually!
# graph.nodes[cluster_id]["applied_constraints"] = [clusters[cluster_id]["applied_constraints"]]
self.G.nodes[cluster_id]["applied_constraints"] = []
####################################################
self.G.nodes[cluster_id]["autoconstraints"] = {}
# Used to track changes in the autoconstraint assignment according to segmentation and proximity style constraint assignment.
self.G.nodes[cluster_id]["autoconstraint_transitions"] = []
self.G.nodes[cluster_id]["model"] = KDEModel(kernel='gaussian',
bandwidth=bandwidth)
self.G.add_path(self.G.nodes())
if vectorizor is not None:
self.G.fit_models(vectorizor)
else:
self.G.fit_models(get_observation_joint_vector)
self.G.identify_primal_observations(get_observation_joint_vector)
def build_keyframe_graph(self, demonstrations, bandwidth):
self.G = KeyframeGraph()
keyframe_clustering = KeyframeClustering()
"""
Generate clusters using labeled observations, build the models, graphs, and attributes for each
cluster in the KeyFrameGraph
"""
clusters = keyframe_clustering.get_clusters(demonstrations)
for cluster_id in clusters.keys():
self.G.add_node(cluster_id)
self.G.nodes[cluster_id]["observations"] = clusters[cluster_id][
"observations"]
self.G.nodes[cluster_id]["keyframe_type"] = clusters[cluster_id][
"keyframe_type"]
self.G.nodes[cluster_id]["applied_constraints"] = []
self.G.nodes[cluster_id]["model"] = KDEModel(kernel='gaussian',
bandwidth=bandwidth)
self.G.add_path(self.G.nodes())
self.G.fit_models(get_observation_joint_vector)
self.G.identify_primal_observations(get_observation_joint_vector)
def main():
arg_fmt = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(formatter_class=arg_fmt,
description=main.__doc__)
required = parser.add_argument_group('required arguments')
required.add_argument(
'-c',
'--config',
dest='config',
required=True,
help='the file path of configuration config.json file ')
required.add_argument(
'-d',
'--directory',
dest='directory',
required=True,
help=
'the directory from which to input labeled demonstration .json files')
parser.add_argument('-b',
'--bandwidth',
type=float,
default=.025,
metavar='BANDWIDTH',
help='gaussian kernel density bandwidth')
parser.add_argument('-t',
'--threshold',
type=int,
default=-1200,
metavar='THRESHOLD',
help='log-liklihood threshold value')
parser.add_argument(
'-n',
'--number_of_samples',
type=int,
default=50,
metavar='NUMBEROFSAMPLES',
help='the number of samples to validate for each keyframe')
args = parser.parse_args(rospy.myargv()[1:])
# Import the data
importer = DataImporter()
labeled_demonstrations = importer.load_json_files(args.directory +
"/*.json")
# Convert imported data into Demonstrations and Observations
demonstrations = []
for datum in labeled_demonstrations["data"]:
observations = []
for entry in datum:
observations.append(Observation(entry))
demonstrations.append(Demonstration(observations))
if len(demonstrations) == 0:
rospy.logwarn("No demonstration data to model!!")
return 0
rospy.init_node("graph_traverse")
""" Create the Cairo LfD environment """
config_filepath = args.config
configs = import_configuration(config_filepath)
items = ItemFactory(configs).generate_items()
constraints = ConstraintFactory(configs).generate_constraints()
# We only have just the one robot...for now.......
environment = Environment(items=items['items'],
robot=items['robots'][0],
constraints=constraints,
triggers=None)
""" Create the moveit_interface """
moveit_interface = SawyerMoveitInterface()
moveit_interface.set_velocity_scaling(.35)
moveit_interface.set_acceleration_scaling(.25)
""" Create KeyframeGraph object. """
graph = KeyframeGraph()
cluster_generator = ObservationClusterer()
"""
Generate clusters using labeled observations, build the models, graphs, and atributes for each
cluster in the KeyFrameGraph
"""
clusters = cluster_generator.generate_clusters(demonstrations)
for cluster_id in clusters.keys():
graph.add_node(cluster_id)
graph.nodes[cluster_id]["observations"] = clusters[cluster_id][
"observations"]
graph.nodes[cluster_id]["keyframe_type"] = clusters[cluster_id][
"keyframe_type"]
graph.nodes[cluster_id]["applied_constraints"] = clusters[cluster_id][
"applied_constraints"]
graph.nodes[cluster_id]["meta_constraints"] = {}
graph.nodes[cluster_id]["model"] = KDEModel(kernel='gaussian',
bandwidth=args.bandwidth)
graph.add_path(graph.nodes())
graph.fit_models(get_observation_joint_vector)
graph._identify_primal_observations(get_observation_joint_vector)
rospy.loginfo(graph.get_keyframe_sequence())
for node in graph.get_keyframe_sequence():
print("KEYFRAME: {}".format(node))
print(graph.nodes[node]["keyframe_type"])
print(graph.nodes[node]["applied_constraints"])
print
# Create height segmentation and heuristic model
position_vectorizor = partial(vectorize_demonstration,
vectorizors=[vectorize_robot_position])
position_vectors = np.array(map(position_vectorizor, demonstrations))
# stack all observation vectors
X = np.vstack(position_vectors)
height_segment_model = BayesianGMMSegmentModel(X, n_components=10)
height_heuristic = HeightHeuristicModel(height_segment_model)
height_heuristic.fit()
height_static_parameters = {
"item_id": 1,
"reference_height": 0,
"direction": "positive"
}
height_metaconstraint_builder = HeightMetaconstraintBuilder(
height_heuristic, height_static_parameters)
metaconstraint_assigner = MetaconstraintAssigner(
environment, graph, [height_metaconstraint_builder])
metaconstraint_assigner.assign_metaconstraints()
""" Build a ConstraintAnalyzer and KeyframeGraphAnalyzer """
constraint_analyzer = ConstraintAnalyzer(environment)
graph_analyzer = KeyframeGraphAnalyzer(graph, moveit_interface,
get_observation_joint_vector)
sample_to_obsv_converter = SawyerSampleConverter(moveit_interface)
sampler = KeyframeSampler(constraint_analyzer, sample_to_obsv_converter)
""" Generate raw_samples from graph for each keyframe """
for node in graph.get_keyframe_sequence():
n_samples = args.number_of_samples
constraints = [
meta.constraints[4]
for meta in graph.nodes[node]["metaconstraints"]
]
for constraint in constraints:
print constraint
attempts, samples, matched_ids = sampler.generate_n_valid_samples(
graph.nodes[node]["model"],
graph.nodes[node]["primal_observation"],
constraints,
n=n_samples)
rospy.loginfo("Keyframe %d: %s valid of %s attempts", node,
len(samples), attempts)
if len(samples) < n_samples:
rospy.loginfo("Keyframe %d: only %s of %s waypoints provided",
node, len(samples), n_samples)
if len(samples) == 0:
# TODO: DOWN SAMPLE METACONSTRAINTS AND KEEP TESTING
rospy.loginfo("Keyframe %d has no valid sample observations", node)
rospy.loginfo("Sampling with no meta constraints")
attempts, samples, matched_ids = sampler.generate_n_valid_samples(
graph.nodes[node]["model"],
graph.nodes[node]["primal_observation"], [],
n=n_samples)
# Order sampled points based on their intra-model log-likelihood
ranked_samples = sampler.rank_samples(graph.nodes[node]["model"],
samples)
# User converter object to convert raw sample vectors into LfD observations
graph.nodes[node]["samples"] = [
sample_to_obsv_converter.convert(sample, run_fk=True)
for sample in ranked_samples
]
""" Clear occluded points (points in collision etc,.) """
for node in graph.get_keyframe_sequence():
samples = graph.nodes[node]["samples"]
free_samples, trash = graph_analyzer.evaluate_keyframe_occlusion(
samples)
if free_samples == []:
rospy.loginfo(
"Keyframe {} has no free samples and will be culled.".format(
node))
graph.cull_node(node)
else:
graph.nodes[node]["free_samples"] = free_samples
""" Cull/remove keyframes/nodes that via change point estimation using log-likelihood """
graph_analyzer.cull_keyframes(threshold=args.threshold)
# """ Order sampled points based on their intra-model log-likelihood """
# for node in graph.get_keyframe_sequence():
# graph.rank_waypoint_samples(node)
output = []
""" Create a sequence of keyframe way points and execute motion plans to reconstruct skill """
joint_config_array = []
for node in graph.get_keyframe_sequence():
output.append((node, graph.nodes[node]["applied_constraints"]))
sample = graph.nodes[node]["free_samples"][0]
joints = sample.get_joint_angle()
joint_config_array.append(joints)
print output
# moveit_interface.move_to_joint_targets(joint_config_array)
return 0
def main():
arg_fmt = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(formatter_class=arg_fmt,
description=main.__doc__)
required = parser.add_argument_group('required arguments')
required.add_argument(
'-c', '--config', dest='config', required=True,
help='the file path of configuration config.json file '
)
required.add_argument(
'-d', '--directory', dest='directory', required=True,
help='the directory from which to input labeled demonstration .json files'
)
parser.add_argument(
'-b', '--bandwidth', type=float, default=.025, metavar='BANDWIDTH',
help='gaussian kernel density bandwidth'
)
parser.add_argument(
'-t', '--threshold', type=int, default=, metavar='THRESHOLD',
help='Kullbach-Leibler divergence threshold value - optional'
)
parser.add_argument(
'-n', '--number_of_samples', type=int, default=50, metavar='NUMBEROFSAMPLES',
help='the number of samples to validate for each keyframe'
)
args = parser.parse_args(rospy.myargv()[1:])
# Import the data
importer = DataImporter()
labeled_demonstrations = importer.load_json_files(args.directory + "/*.json")
# Convert imported data into Demonstrations and Observations
demonstrations = []
for datum in labeled_demonstrations["data"]:
observations = []
for entry in datum:
observations.append(Observation(entry))
demonstrations.append(Demonstration(observations))
if len(demonstrations) == 0:
rospy.logwarn("No demonstration data to model!!")
return 0
rospy.init_node("graph_traverse")
""" Create the Cairo LfD environment """
config_filepath = args.config
configs = import_configuration(config_filepath)
items = ItemFactory(configs).generate_items()
constraints = ConstraintFactory(configs).generate_constraints()
# We only have just the one robot...for now.......
environment = Environment(items=items['items'], robot=items['robots'][0], constraints=constraints, triggers=None)
""" Create the moveit_interface """
moveit_interface = SawyerMoveitInterface()
moveit_interface.set_velocity_scaling(.35)
moveit_interface.set_acceleration_scaling(.25)
""" Create KeyframeGraph object. """
graph = KeyframeGraph()
cluster_generator = ObservationClusterer()
"""
Generate clusters using labeled observations, build the models, graphs, and atributes for each
cluster in the KeyFrameGraph
"""
clusters = cluster_generator.generate_clusters(demonstrations)
for cluster_id in clusters.keys():
graph.add_node(cluster_id)
graph.nodes[cluster_id]["observations"] = clusters[cluster_id]["observations"]
graph.nodes[cluster_id]["keyframe_type"] = clusters[cluster_id]["keyframe_type"]
graph.nodes[cluster_id]["applied_constraints"] = clusters[cluster_id]["applied_constraints"]
graph.nodes[cluster_id]["model"] = KDEModel(kernel='gaussian', bandwidth=args.bandwidth)
graph.add_path(graph.nodes())
graph.fit_models(get_observation_joint_vector)
graph._identify_primal_observations(get_observation_joint_vector)
rospy.loginfo(graph.get_keyframe_sequence())
for node in graph.get_keyframe_sequence():
print("KEYFRAME: {}".format(node))
print(graph.nodes[node]["keyframe_type"])
print(graph.nodes[node]["applied_constraints"])
print
""" Build a ConstraintAnalyzer and KeyframeGraphAnalyzer """
constraint_analyzer = ConstraintAnalyzer(environment)
graph_analyzer = KeyframeGraphAnalyzer(graph, moveit_interface, get_observation_joint_vector)
sample_to_obsv_converter = SawyerSampleConverter(moveit_interface)
sampler = KeyframeSampler(constraint_analyzer, sample_to_obsv_converter)
model_score_ranker = ModelScoreSampleRanker()
configraution_ranker = ConfigurationSpaceSampleRanker()
""" Generate raw_samples from graph for each keyframe """
prior_sample = None
for node in graph.get_keyframe_sequence():
print "Keyframe {}".format(node)
# Keep sampling
if graph.nodes[node]["keyframe_type"] == "constraint_transition":
rospy.loginfo("Sampling from a constraint transition keyframe.")
constraints = [environment.get_constraint_by_id(constraint_id) for constraint_id in graph.nodes[node]["applied_constraints"]]
attempts, samples, matched_ids = sampler.generate_n_valid_samples(graph.nodes[node]["model"], graph.nodes[node]["primal_observation"], constraints, n=n_samples)
if len(samples) == 0:
# Some constraints couldn't be sampled successfully, so using best available samples.
diff = list(set(graph.nodes[node]["applied_constraints"]).difference(set(matched_ids)))
if len(matched_ids) > 0:
rospy.logwarn("Constraints {} couldn't be met so attempting to find valid samples with constraints {}.".format(diff, matched_ids))
constraints = [environment.get_constraint_by_id(constraint_id) for constraint_id in graph.nodes[node]["applied_constraints"]]
attempts, samples, matched_ids = sampler.generate_n_valid_samples(graph.nodes[node]["model"], graph.nodes[node]["primal_observation"], constraints, n=n_samples)
else:
rospy.logwarn("Constraints {} couldn't be met so. Cannot meet any constraints.".format(diff))
else:
n_samples = args.number_of_samples
constraints = [environment.get_constraint_by_id(constraint_id) for constraint_id in graph.nodes[node]["applied_constraints"]]
attempts, samples, matched_ids = sampler.generate_n_valid_samples(graph.nodes[node]["model"], graph.nodes[node]["primal_observation"], constraints, n=n_samples)
rospy.loginfo("Keyframe %d: %s valid of %s attempts", node, len(samples), attempts)
if len(samples) < n_samples:
rospy.loginfo("Keyframe %d: only %s of %s waypoints provided", node, len(samples), n_samples)
if len(samples) == 0:
rospy.loginfo("Keyframe %d has no valid sample observations", node)
graph.cull_node(node)
else:
# Order sampled points based on their intra-model log-likelihood
if prior_sample is None:
ranked_samples = model_score_ranker.rank(graph.nodes[node]["model"], samples)
else:
ranked_samples = configraution_ranker.rank(graph.nodes[node]["model"], samples, prior_sample)
prior_sample = ranked_samples[0]
# User converter object to convert raw sample vectors into LfD observations
graph.nodes[node]["samples"] = [sample_to_obsv_converter.convert(sample, run_fk=True) for sample in ranked_samples]
""" Clear occluded points (points in collision etc,.) """
for node in graph.get_keyframe_sequence():
samples = graph.nodes[node]["samples"]
free_samples, trash = graph_analyzer.evaluate_keyframe_occlusion(samples)
if free_samples == []:
rospy.loginfo("Keyframe {} has no free samples and will be culled.".format(node))
graph.cull_node(node)
else:
graph.nodes[node]["free_samples"] = free_samples
""" Cull/remove keyframes/nodes that via change point estimation using log-likelihood """
if "automated_culling_threshold" in config['settings']:
automated_threshold = config['settings']['automated_culling_threshold']
else:
automated_culling = True
graph_analyzer.cull_keyframes(threshold=args.threshold, automated=automated_threshold)
# """ Order sampled points based on their intra-model log-likelihood """
# for node in graph.get_keyframe_sequence():
# graph.rank_waypoint_samples(node)
output = []
""" Create a sequence of keyframe way points and execute motion plans to reconstruct skill """
joint_config_array = []
for node in graph.get_keyframe_sequence():
output.append((node, graph.nodes[node]["applied_constraints"]))
sample = graph.nodes[node]["free_samples"][0]
joints = sample.get_joint_angle()
joint_config_array.append(joints)
print output
moveit_interface.move_to_joint_targets(joint_config_array)
return 0