def get_parameter_result(sim_world, task, parameter_fns, evaluation_fn,
**kwargs):
start_time = time.time()
plan, plan_time = None, 0
while (plan is None) and (elapsed_time(start_time) < kwargs['max_time']):
plan, plan_time = simulate_trial(sim_world, task, parameter_fns,
**kwargs)
score = None
if plan is None:
print('No plan was found in {} seconds.'.format(kwargs['max_time']))
control = {'feature': None, 'parameter': None}
else:
score = evaluation_fn(plan)
# _, args = find_unique(lambda a: a[0] == 'pour', plan)
# control = args[-1]
elapsed = elapsed_time(start_time)
result = {
'success': (plan is not None),
'score': score,
'plan-time': plan_time,
'elapsed-time': elapsed, # Includes execution time
# 'feature': control['feature'],
# 'parameter': control['parameter']
}
return result
def find_closest(x0,
curve,
t_range=None,
max_time=INF,
max_iterations=INF,
distance_fn=None,
verbose=False):
assert (max_time < INF) or (max_iterations < INF)
if t_range is None:
t_range = Interval(curve.x[0], curve.x[-1])
t_range = Interval(max(t_range[0], curve.x[0]),
min(curve.x[-1], t_range[-1]))
if distance_fn is None:
distance_fn = get_distance
start_time = time.time()
closest_dist, closest_t = INF, None
for iteration in irange(max_iterations):
if elapsed_time(start_time) >= max_time:
break
t = random.uniform(*t_range) # TODO: halton
x = curve(t)
dist = distance_fn(x0, x)
if dist < closest_dist:
closest_dist, closest_t = dist, t
if verbose:
print(
'Iteration: {} | Dist: {:.3f} | T: {:.3f} | Time: {:.3f}'.
format(iteration, closest_dist, t,
elapsed_time(start_time)))
return closest_dist, closest_t
def random_restart(belief, args, problem, max_time=INF, max_iterations=INF,
max_planner_time=INF, **kwargs):
domain_pddl, constant_map, _, _, init, goal_formula = problem
start_time = time.time()
task = belief.task
world = task.world
iterations = 0
while (elapsed_time(start_time) < max_time) and (iterations < max_iterations):
iterations += 1
try:
stream_pddl, stream_map = get_streams(world, teleport_base=task.teleport_base,
collisions=not args.cfree, teleport=args.teleport)
problem = PDDLProblem(domain_pddl, constant_map, stream_pddl, stream_map, init, goal_formula)
remaining_time = min(max_time - elapsed_time(start_time), max_planner_time)
plan, plan_cost, certificate = solve_pddlstream(belief, problem, args, max_time=remaining_time, **kwargs)
if plan is not None:
return plan, plan_cost, certificate
except KeyboardInterrupt as e:
raise e
except MemoryError as e:
traceback.print_exc()
if not REPLAN_FAILURES:
raise e
break
except Exception as e:
traceback.print_exc()
if not REPLAN_FAILURES:
raise e
# FastDownward translator runs out of memory
return None, INF, Certificate(all_facts=[], preimage_facts=[])
def optimize_commands(robot, obstacles, element_bodies, extrusion_path, initial_conf, commands,
max_iterations=INF, max_time=60,
motions=True, collisions=True, **kwargs):
if commands is None:
return None
start_time = time.time()
element_from_id, node_points, ground_nodes = load_extrusion(extrusion_path)
print_gen_fn = get_print_gen_fn(robot, obstacles, node_points, element_bodies, ground_nodes,
precompute_collisions=False, collisions=collisions, **kwargs)
commands = list(commands)
sequence = [command.elements[0] for command in commands]
directed_sequence = [command.directed_elements[0] for command in commands]
indices = list(range(len(sequence)))
#trajectories = flatten_commands(commands)
#print(len(trajectories), get_print_distance(trajectories, teleport=False))
total_distance = sum(get_command_distance(robot, initial_conf, commands, index, command)
for index, command in enumerate(commands))
# TODO: bias sampling towards far apart relative to neighoors
# TODO: bias IK solutions to be the best of several
iterations = extrusion_failures = 0
while (iterations < max_iterations) and (elapsed_time(start_time) < max_time):
iterations += 1
index = random.choice(indices)
printed = sequence[:index-1]
element = sequence[index]
directed = directed_sequence[index]
print(commands[index])
distance = get_command_distance(robot, initial_conf, commands, index, commands[index])
print('Iteration {} | Failures: {} | Distance: {:.3f} | Index: {}/{} | Time: {:.3f}'.format(
iterations, extrusion_failures, total_distance, index, len(indices), elapsed_time(start_time)))
new_command, = next(print_gen_fn(directed[0], element, extruded=printed), (None,))
if new_command is None:
extrusion_failures += 1
continue
new_distance = get_command_distance(robot, initial_conf, commands, index, new_command)
if new_distance < distance:
commands.pop(index)
commands.insert(index, new_command)
total_distance -= (distance - new_distance)
# data = {
# 'extrusion_failures': extrusion_failures,
# }
return commands #, data
def create_belief(self):
t0 = time.time()
print('Creating initial belief')
belief = create_surface_belief(self.world, self.prior)
belief.task = self
print('Took {:2f} seconds'.format(elapsed_time(t0)))
return belief
def compute_component_mst(node_points,
ground_nodes,
unprinted,
initial_position=None):
# Weighted A*
start_time = time.time()
point_from_vertex = dict(enumerate(node_points))
vertices = {v for e in unprinted for v in e}
components = get_connected_components(vertices, unprinted)
entry_nodes = set(ground_nodes)
if initial_position is not None:
point_from_vertex[INITIAL_NODE] = initial_position
components.append([INITIAL_NODE])
entry_nodes.add(INITIAL_NODE)
edge_weights = {}
for c1, c2 in combinations(range(len(components)), r=2):
# TODO: directed edges from all points to entry nodes
nodes1 = set(components[c1])
nodes2 = set(components[c2])
if (c1 == [INITIAL_NODE]) or (c2 == [INITIAL_NODE]):
nodes1 &= entry_nodes
nodes2 &= entry_nodes
edge_weights[c1, c2] = min(
get_distance(point_from_vertex[n1], point_from_vertex[n2])
for n1, n2 in product(nodes1, nodes2))
tree = compute_spanning_tree(edge_weights)
weight = sum(edge_weights[e] for e in tree)
print(
'Elements: {} | Components: {} | Tree: {} | Weight: {}: Time: {:.3f} '.
format(len(unprinted), len(components), tree, weight,
elapsed_time(start_time)))
return weight
def evaluate_scores(domain,
seed,
algorithm,
learner,
num_trials,
serial=False,
visualize=False):
results = []
if num_trials == 0:
return results
start_time = time.time()
# trials selected by learner, num_trials is number of contexts that the learner evaluated upon
trials = get_trials(problem=domain.skill,
fn=learner,
num_trials=num_trials,
seed=seed,
visualize=visualize,
verbose=serial)
num_cores = get_num_cores(trials, serial=serial)
results = run_trials(trials, num_cores=num_cores)
scored = get_scored_results(results)
scores = [
domain.score_fn(result[FEATURE], result[PARAMETER], result[SCORE])
for result in scored
]
# TODO: record best predictions as well as average prediction quality
print('Seed: {} | {} | Results: {} | Scored: {} | Time: {:.3f}'.format(
seed, algorithm, len(results), len(scored), elapsed_time(start_time)))
if scores:
print('Average: {:.3f} | Success: {:.1f}%'.format(
np.average(scores), 100 * score_accuracy(scores)))
return results
def local_search(extrusion_path,
element_from_id,
node_points,
ground_nodes,
checker,
sequence,
initial_position=None,
stiffness=True,
max_time=INF):
start_time = time.time()
sequence = list(sequence)
#elements = set(element_from_id.values())
#indices = list(range(len(sequence)))
#directions = [True, False]
while elapsed_time(start_time) < max_time:
new_sequence = search_neighborhood(extrusion_path,
element_from_id,
node_points,
ground_nodes,
checker,
sequence,
initial_position,
stiffness=stiffness,
max_time=INF)
if new_sequence is None:
break
sequence = new_sequence
def create_inverse_reachability(robot, body, table, arm, grasp_type, max_attempts=500, num_samples=500):
tool_link = get_gripper_link(robot, arm)
robot_saver = BodySaver(robot)
gripper_from_base_list = []
grasps = GET_GRASPS[grasp_type](body)
start_time = time.time()
while len(gripper_from_base_list) < num_samples:
box_pose = sample_placement(body, table)
set_pose(body, box_pose)
grasp_pose = random.choice(grasps)
gripper_pose = multiply(box_pose, invert(grasp_pose))
for attempt in range(max_attempts):
robot_saver.restore()
base_conf = next(uniform_pose_generator(robot, gripper_pose)) #, reachable_range=(0., 1.)))
#set_base_values(robot, base_conf)
set_group_conf(robot, 'base', base_conf)
if pairwise_collision(robot, table):
continue
grasp_conf = pr2_inverse_kinematics(robot, arm, gripper_pose) #, nearby_conf=USE_CURRENT)
#conf = inverse_kinematics(robot, link, gripper_pose)
if (grasp_conf is None) or pairwise_collision(robot, table):
continue
gripper_from_base = multiply(invert(get_link_pose(robot, tool_link)), get_base_pose(robot))
#wait_if_gui()
gripper_from_base_list.append(gripper_from_base)
print('{} / {} | {} attempts | [{:.3f}]'.format(
len(gripper_from_base_list), num_samples, attempt, elapsed_time(start_time)))
wait_if_gui()
break
else:
print('Failed to find a kinematic solution after {} attempts'.format(max_attempts))
return save_inverse_reachability(robot, arm, grasp_type, tool_link, gripper_from_base_list)
def compute_motions(robot, fixed_obstacles, element_bodies, initial_conf, print_trajectories, **kwargs):
# TODO: reoptimize for the sequence that have the smallest movements given this
# TODO: sample trajectories
# TODO: more appropriate distance based on displacement/volume
if print_trajectories is None:
return None
#if any(isinstance(print_traj, MotionTrajectory) for print_traj in print_trajectories):
# return print_trajectories
start_time = time.time()
printed_elements = []
all_trajectories = []
current_conf = initial_conf
for i, print_traj in enumerate(print_trajectories):
if not np.allclose(current_conf, print_traj.start_conf, rtol=0, atol=1e-8):
motion_traj = compute_motion(robot, fixed_obstacles, element_bodies,
printed_elements, current_conf, print_traj.start_conf, **kwargs)
if motion_traj is None:
return None
print('{}) {} | Time: {:.3f}'.format(i, motion_traj, elapsed_time(start_time)))
all_trajectories.append(motion_traj)
if isinstance(print_traj, PrintTrajectory):
printed_elements.append(print_traj.element)
all_trajectories.append(print_traj)
current_conf = print_traj.end_conf
motion_traj = compute_motion(robot, fixed_obstacles, element_bodies,
printed_elements, current_conf, initial_conf, **kwargs)
if motion_traj is None:
return None
return all_trajectories + [motion_traj]
def create_inverse_reachability2(robot, body, table, arm, grasp_type, max_attempts=500, num_samples=500):
tool_link = get_gripper_link(robot, arm)
problem = MockProblem(robot, fixed=[table], grasp_types=[grasp_type])
placement_gen_fn = get_stable_gen(problem)
grasp_gen_fn = get_grasp_gen(problem, collisions=True)
ik_ir_fn = get_ik_ir_gen(problem, max_attempts=max_attempts, learned=False, teleport=True)
placement_gen = placement_gen_fn(body, table)
grasps = list(grasp_gen_fn(body))
print('Grasps:', len(grasps))
# TODO: sample the torso height
# TODO: consider IK with respect to the torso frame
start_time = time.time()
gripper_from_base_list = []
while len(gripper_from_base_list) < num_samples:
[(p,)] = next(placement_gen)
(g,) = random.choice(grasps)
output = next(ik_ir_fn(arm, body, p, g), None)
if output is None:
print('Failed to find a solution after {} attempts'.format(max_attempts))
else:
(_, ac) = output
[at,] = ac.commands
at.path[-1].assign()
gripper_from_base = multiply(invert(get_link_pose(robot, tool_link)), get_base_pose(robot))
gripper_from_base_list.append(gripper_from_base)
print('{} / {} [{:.3f}]'.format(
len(gripper_from_base_list), num_samples, elapsed_time(start_time)))
wait_if_gui()
return save_inverse_reachability(robot, arm, grasp_type, tool_link, gripper_from_base_list)
def pour_beads(world,
cup_name,
beads,
reset_contained=False,
fix_outside=True,
cup_thickness=0.01,
bead_offset=0.01,
drop_rate=0.02,
**kwargs):
if not beads:
return set()
start_time = time.time()
# TODO: compute the radius of each bead
bead_radius = np.average(approximate_as_prism(beads[0])) / 2.
masses = list(map(get_mass, beads))
savers = list(map(BodySaver, beads))
for body in beads:
set_mass(body, 0)
cup = world.get_body(cup_name)
local_center, (diameter, height) = approximate_as_cylinder(cup)
center = get_point(cup) + local_center
interior_radius = max(0.0, diameter / 2. - bead_radius - cup_thickness)
# TODO: fill up to a certain threshold
ty = get_type(cup_name)
if ty in SPOON_DIAMETERS:
# TODO: do this in a more principled way
interior_radius = 0
center[1] += (SPOON_LENGTHS[ty] - SPOON_DIAMETERS[ty]) / 2.
# TODO: some sneak out through the bottom
# TODO: could reduce gravity while filling
world.controller.set_gravity()
for i, bead in enumerate(beads):
# TODO: halton sequence
x, y = center[:2] + np.random.uniform(
0, interior_radius) * unit_from_theta(
np.random.uniform(-np.pi, np.pi))
new_z = center[2] + height / 2. + bead_radius + bead_offset
set_point(bead, [x, y, new_z])
set_mass(bead, masses[i])
world.controller.rest_for_duration(drop_rate)
world.controller.rest_for_duration(BEADS_REST)
print('Simulated {} beads in {:3f} seconds'.format(
len(beads), elapsed_time(start_time)))
contained_beads = get_contained_beads(cup, beads, **kwargs)
#wait_for_user()
for body in beads:
if fix_outside and (body not in contained_beads):
set_mass(body, 0)
for saver in savers:
if reset_contained or (saver.body not in contained_beads):
saver.restore()
#wait_for_user()
return contained_beads
def optimize_feature(learner, max_time=INF, **kwargs):
#features = read_json(get_feature_path(learner.func.skill))
feature_fn, attributes, pairs = generate_candidates(
learner.func.skill, **kwargs)
start_time = time.time()
world = ROSWorld(use_robot=False, sim_only=True)
saver = ClientSaver(world.client)
print('Optimizing over {} feature pairs'.format(len(pairs)))
best_pair, best_score = None, -INF # maximize
for pair in randomize(pairs): # islice
if max_time <= elapsed_time(start_time):
break
world.reset(keep_robot=False)
for name in pair:
world.perception.add_item(name)
feature = feature_fn(world, *pair)
parameter = next(
learner.parameter_generator(world,
feature,
min_score=best_score,
valid=True,
verbose=False), None)
if parameter is None:
continue
context = learner.func.context_from_feature(feature)
sample = learner.func.sample_from_parameter(parameter)
x = x_from_context_sample(context, sample)
#x = learner.func.x_from_feature_parameter(feature, parameter)
score_fn = learner.get_score_f(context, noise=False,
negate=False) # maximize
score = float(score_fn(x)[0, 0])
if best_score < score:
best_pair, best_score = pair, score
world.stop()
saver.restore()
print(
'Best pair: {} | Best score: {:.3f} | Pairs: {} | Time: {:.3f}'.format(
best_pair, best_score, len(pairs), elapsed_time(start_time)))
assert best_score is not None
# TODO: ensure the same parameter is used
return dict(zip(attributes, best_pair))
def mpc(x0,
v0,
curve,
dt_max=0.5,
max_time=INF,
max_iterations=INF,
v_max=None,
**kwargs):
assert (max_time < INF) or (max_iterations < INF)
from scipy.interpolate import CubicHermiteSpline
start_time = time.time()
best_cost, best_spline = INF, None
for iteration in irange(max_iterations):
if elapsed_time(start_time) >= max_time:
break
t1 = random.uniform(curve.x[0], curve.x[-1])
future = (curve.x[-1] - t1) # TODO: weighted
if future >= best_cost:
continue
x1 = curve(t1)
if (v_max is not None) and (max((x1 - x0) / v_max) > dt_max):
continue
# if quickest_inf_accel(x0, x1, v_max=v_max) > dt_max:
# continue
v1 = curve(t1, nu=1)
#dt = dt_max
dt = random.uniform(0, dt_max)
times = [0., dt]
positions = [x0, x1]
velocities = [v0, v1]
spline = CubicHermiteSpline(times, positions, dydx=velocities)
if not check_spline(spline, **kwargs):
continue
# TODO: optimize to find the closest on the path within a range
cost = future + (spline.x[-1] - spline.x[0])
if cost < best_cost:
best_cost, best_spline = cost, spline
print('Iteration: {} | Cost: {:.3f} | T: {:.3f} | Time: {:.3f}'.
format(iteration, cost, t1, elapsed_time(start_time)))
print(best_cost, t1, elapsed_time(start_time))
return best_cost, best_spline
def wait_until_observation(problem, ros_world, max_time=5):
ros_world.sync_controllers() # AKA update sim robot joint positions
iteration = count()
start_time = time.time()
ros_world.perception.updated_simulation = False
while elapsed_time(start_time) < max_time:
ros_world.perception.wait_for_update()
names = set(info.name for info in ros_world.perception.surfaces +
ros_world.perception.items)
items = REQUIREMENT_FNS[problem](ros_world)
print('Iteration: {} | Bodies: {} | Time: {:.3f}'.format(
next(iteration), sorted(names), elapsed_time(start_time)))
if items is not None:
break
else:
print('Failed to detect required objects for {}'.format(problem))
return None
ros_world.perception.update_simulation()
# TODO: return the detected items
return items
def retrain_sklearer(learner, newx=None, newy=None, new_w=None):
start_time = time.time()
if (newx is not None) and (newy is not None):
learner.xx = np.vstack((learner.xx, newx))
if learner.model_type in CLASSIFIERS:
newy = (THRESHOLD < newy)
learner.yy = np.hstack((learner.yy, newy))
if new_w is not None:
learner.weights = np.hstack((learner.weights, new_w))
# TODO: what was this supposed to do?
#self.xx, self.yy, sample_weight = shuffle(self.xx, self.yy, _get_sample_weight(self))
if learner.model.__class__ in [MLPClassifier, MLPRegressor]:
learner.model.fit(learner.xx, learner.yy)
else:
# TODO: preprocess to change the mean_function
# https://scikit-learn.org/stable/modules/preprocessing.html
xx, yy = learner.xx, learner.yy
weights = None
if (learner.transfer_weight
is not None) and (2 <= len(Counter(learner.weights))):
assert 0. <= learner.transfer_weight <= 1.
#weights = learner.weights
weights = np.ones(yy.shape)
total_weight = sum(weights)
normal_indices = np.argwhere(learner.weights == NORMAL_WEIGHT)
weights[normal_indices] = total_weight * (
1 - learner.transfer_weight) / len(normal_indices)
transfer_indices = np.argwhere(learner.weights == TRANSFER_WEIGHT)
weights[
transfer_indices] = total_weight * learner.transfer_weight / len(
transfer_indices)
print(
'Transfer weight: {:.3f} | Normal: {} | Transfer: {} | Other: {}'
.format(
learner.transfer_weight, len(normal_indices),
len(transfer_indices),
len(weights) - len(normal_indices) -
len(transfer_indices)))
#weights = 1./len(yy) * np.ones(len(yy))
#num_repeat = 0
# if num_repeat != 0:
# xx = np.vstack([xx, xx[:num_repeat]])
# yy = np.vstack([yy, yy[:num_repeat]])
# weights = np.concatenate([
# 1./len(yy) * np.ones(len(yy)),
# 1./num_repeat * np.ones(num_repeat),
# ])
learner.model.fit(xx, yy, sample_weight=weights)
print('Trained in {:.3f} seconds'.format(elapsed_time(start_time)))
learner.metric()
learner.trained = True
def run_trials(trials, data_path=None, num_cores=False, **kwargs):
# https://stackoverflow.com/questions/15314189/python-multiprocessing-pool-hangs-at-join
# https://stackoverflow.com/questions/39884898/large-amount-of-multiprocessing-process-causing-deadlock
# TODO: multiprocessing still seems to hang on one thread before starting
assert (get_python_version() == 3)
results = []
if not trials:
return results
start_time = time.time()
serial = (num_cores is False) # None is the max number
failures = 0
scored = 0
try:
for result in map_general(run_trial,
trials,
serial,
num_cores=num_cores,
**kwargs):
num_trials = len(results) + failures
print(
'{}\nTrials: {} | Successes: {} | Failures: {} | Scored: {} | Time: {:.3f}'
.format(SEPARATOR, num_trials, len(results), failures, scored,
elapsed_time(start_time)))
print('Result:', str_from_object(result))
if result is None:
failures += 1
print('Error! Trial resulted in an exception')
continue
scored += int(result.get('score', None) is not None)
results.append(result)
write_results(data_path, results)
# except BaseException as e:
# traceback.print_exc() # e
finally:
print(SEPARATOR)
safe_rm_dir(TEMP_DIRECTORY)
write_results(data_path, results)
print('Hours: {:.3f}'.format(elapsed_time(start_time) / HOURS_TO_SECS))
# TODO: make a generator version of this
return results
def simulate(controller=None,
max_duration=INF,
max_steps=INF,
print_rate=1.,
sleep=None):
enable_gravity()
dt = get_time_step()
print('Time step: {:.6f} sec'.format(dt))
start_time = last_print = time.time()
for step in irange(max_steps):
duration = step * dt
if duration >= max_duration:
break
if (controller is not None) and is_empty(controller):
break
step_simulation()
synchronize_viewer()
if elapsed_time(last_print) >= print_rate:
print(
'Sim step: {} | Sim time: {:.3f} sec | Elapsed time: {:.3f} sec'
.format(step, duration, elapsed_time(start_time)))
last_print = time.time()
if sleep is not None:
time.sleep(sleep)
def search_neighborhood(extrusion_path,
element_from_id,
node_points,
ground_nodes,
checker,
sequence,
initial_position,
stiffness=True,
max_candidates=1,
max_time=INF):
start_time = time.time()
best_sequence = None
best_cost = compute_sequence_distance(node_points,
sequence,
start=initial_position,
end=initial_position)
candidates = 1
for i1, i2 in randomize(
combinations_with_replacement(range(len(sequence)), r=2)):
for directed1 in get_directions(sequence[i1]):
for directed2 in get_directions(sequence[i2]):
if implies(best_sequence, (max_candidates <= candidates)) and (
max_time <= elapsed_time(start_time)):
return best_sequence
candidates += 1
if i1 == i2:
new_sequence = sequence[:i1] + [directed1
] + sequence[i1 + 1:]
else:
new_sequence = sequence[:i1] + [
directed1
] + sequence[i1 + 1:i2] + [directed2] + sequence[i2 + 1:]
assert len(new_sequence) == len(sequence)
if count_violations(ground_nodes, new_sequence):
continue
new_cost = compute_sequence_distance(node_points,
new_sequence,
start=initial_position,
end=initial_position)
if best_cost <= new_cost:
continue
print(best_cost, new_cost)
return new_sequence
# TODO: eager version of this also
# if stiffness and not test_stiffness(extrusion_path, element_from_id, printed, checker=checker, verbose=False):
# continue # Unfortunately the full structure is affected
return best_sequence
def solve_pybullet_ik(self, world_from_tool, nearby_tolerance):
start_time = time.time()
# Most of the time is spent creating the robot
# TODO: use the waypoint version that doesn't repeatedly create the robot
current_conf = get_joint_positions(self.robot, self.arm_joints)
full_conf = sub_inverse_kinematics(
self.robot,
self.arm_joints[0],
self.tool_link,
world_from_tool,
custom_limits=self.custom_limits
) # , max_iterations=1) # , **kwargs)
conf = get_joint_positions(self.robot, self.arm_joints)
max_distance = get_distance(current_conf, conf, norm=INF)
if nearby_tolerance < max_distance:
return None
print('Nearby) time: {:.3f} | distance: {:.5f}'.format(
elapsed_time(start_time), max_distance))
return full_conf
def evaluate_learner(domain, seed, train_confusion,
X_test, Y_test, algorithm, learner,
train_size, num_trials, alphas,
serial=False, confusion_only=True):
# learner select train_size number of trials?
start_time = time.time()
max_cores = get_max_cores(serial=serial)
test_confusion = None
if (train_size is not None) and (len(Y_test) != 0):
# TODO: kernel density estimation
print('Test examples:', Y_test.shape[0])
if algorithm.name in NN_MODELS:
alphas = alphas[:1]
confusions = []
for alpha in alphas:
print('Alpha={}'.format(alpha) if alpha is None else 'Alpha={:.3f}'.format(alpha))
confusions.append(compute_metrics(Y_test, learner.score_x(
X_test, alpha=alpha, max_cores=max_cores)))
test_confusion = confusions[0]
results = []
if not confusion_only:
# trials selected by learner, num_trials is number of contexts that the learner evaluated upon
trials = get_trials(problem=domain.skill, fn=learner, num_trials=num_trials,
seed=seed, verbose=serial)
num_cores = get_num_cores(trials, serial=serial)
results = run_trials(trials, num_cores=num_cores)
scores = [domain.score_fn(result[FEATURE], result[PARAMETER], result[SCORE])
for result in results]
# TODO: record best predictions as well as average prediction quality
print('Seed: {} | {} | Results: {} | Time: {:.3f}'.format(
seed, algorithm, len(results), elapsed_time(start_time)))
if scores:
print('Average: {:.3f} | Success: {:.1f}%'.format(
np.average(scores), 100 * score_accuracy(scores)))
confusion = {
'train': train_confusion,
'test': test_confusion,
}
return Experiment(algorithm, train_size, confusion, results)
def update(self, detections, n_samples=25):
start_time = time.time()
self.observations.append(detections)
# Processing detected first
# Could simply sample from the set of worlds and update
# Would need to sample many worlds with name at different poses
# Instead, let the moving object take on different poses
with LockRenderer():
with WorldSaver():
if REPAIR_DETECTIONS:
detections = fix_detections(
self, detections) # TODO: skip if in sim
detections = relative_detections(self, detections)
order = [name for name in detections] # Detected
order.extend(set(self.pose_dists) - set(order)) # Not detected
for name in order:
self.pose_dists[name] = self.pose_dists[name].update(
self, detections, n_samples=n_samples)
self.update_state()
print('Update time: {:.3f} sec for {} objects and {} samples'.format(
elapsed_time(start_time), len(order), n_samples))
return self
def compute_euclidean_tree(node_points,
ground_nodes,
elements,
initial_position=None):
# remove printed elements from the tree
# TODO: directed version
start_time = time.time()
point_from_vertex, edges = embed_graph(elements, node_points, ground_nodes,
initial_position)
edge_weights = {(n1, n2): get_distance(point_from_vertex[n1],
point_from_vertex[n2])
for n1, n2 in edges}
components = get_connected_components(point_from_vertex, edge_weights)
tree = compute_spanning_tree(edge_weights)
from extrusion.visualization import draw_model
draw_model(tree, point_from_vertex, ground_nodes, color=BLUE)
draw_model(edges - tree, point_from_vertex, ground_nodes, color=RED)
weight = sum(edge_weights[e] for e in tree)
print(len(components), len(point_from_vertex), len(tree), weight,
elapsed_time(start_time))
wait_for_user()
return weight
def follow_trajectory(self,
joints,
path,
times_from_start=None,
real_time_step=0.0,
waypoint_tolerance=1e-2 * np.pi,
goal_tolerance=5e-3 * np.pi,
max_sim_duration=1.0,
print_sim_frequency=1.0,
**kwargs):
# real_time_step = 1e-1 # Can optionally sleep to slow down visualization
start_time = time.time()
if times_from_start is not None:
assert len(path) == len(times_from_start)
current_positions = get_joint_positions(self.robot, joints)
differences = [(np.array(q2) - np.array(q1)) / (t2 - t1)
for q1, t1, q2, t2 in
zip([current_positions] + path, [0.] +
times_from_start, path, times_from_start)]
velocity_path = differences[1:] + [np.zeros(len(joints))
] # Using velocity at endpoints
else:
velocity_path = [None] * len(path)
sim_duration = 0.0
sim_steps = 0
last_print_sim_duration = sim_duration
with ClientSaver(self.client):
dt = get_time_step()
# TODO: fit using splines to get velocity info
for num, positions in enumerate(path):
if self.execute_motor_control:
sim_start = sim_duration
tolerance = goal_tolerance if (num == len(path) -
1) else waypoint_tolerance
velocities = velocity_path[num]
for _ in joint_controller_hold2(self.robot,
joints,
positions,
velocities,
tolerance=tolerance,
**kwargs):
step_simulation()
# print(get_joint_velocities(self.robot, joints))
# print([get_joint_torque(self.robot, joint) for joint in joints])
sim_duration += dt
sim_steps += 1
time.sleep(real_time_step)
if print_sim_frequency <= (sim_duration -
last_print_sim_duration):
print(
'Waypoint: {} | Simulation steps: {} | Simulation seconds: {:.3f} | Steps/sec {:.3f}'
.format(num, sim_steps, sim_duration,
sim_steps / elapsed_time(start_time)))
last_print_sim_duration = sim_duration
if max_sim_duration <= (sim_duration - sim_start):
print(
'Timeout of {:.3f} simulation seconds exceeded!'
.format(max_sim_duration))
break
# control_joints(self.robot, arm_joints, positions)
else:
set_joint_positions(self.robot, joints, positions)
print(
'Followed {} waypoints in {:.3f} simulation seconds and {} simulation steps'
.format(len(path), sim_duration, sim_steps))
def collect_place(world, object_name, surface_name, grasp_type, args):
date = get_date()
#set_seed(args.seed)
#dump_body(world.robot)
surface_pose = get_surface_reference_pose(world.kitchen, surface_name) # TODO: assumes the drawer is open
stable_gen_fn = get_stable_gen(world, z_offset=Z_EPSILON, visibility=False,
learned=False, collisions=not args.cfree)
grasp_gen_fn = get_grasp_gen(world)
ik_ir_gen = get_pick_gen_fn(world, learned=False, collisions=not args.cfree, teleport=args.teleport)
stable_gen = stable_gen_fn(object_name, surface_name)
grasps = list(grasp_gen_fn(object_name, grasp_type))
robot_name = get_body_name(world.robot)
path = get_place_path(robot_name, surface_name, grasp_type)
print(SEPARATOR)
print('Robot name: {} | Object name: {} | Surface name: {} | Grasp type: {} | Filename: {}'.format(
robot_name, object_name, surface_name, grasp_type, path))
entries = []
start_time = time.time()
failures = 0
while (len(entries) < args.num_samples) and \
(elapsed_time(start_time) < args.max_time): #and (failures <= max_failures):
(rel_pose,) = next(stable_gen)
if rel_pose is None:
break
(grasp,) = random.choice(grasps)
with LockRenderer(lock=True):
result = next(ik_ir_gen(object_name, rel_pose, grasp), None)
if result is None:
print('Failure! | {} / {} [{:.3f}]'.format(
len(entries), args.num_samples, elapsed_time(start_time)))
failures += 1
continue
# TODO: ensure an arm motion exists
bq, aq, at = result
rel_pose.assign()
bq.assign()
aq.assign()
base_pose = get_link_pose(world.robot, world.base_link)
object_pose = rel_pose.get_world_from_body()
tool_pose = multiply(object_pose, invert(grasp.grasp_pose))
entries.append({
'tool_from_base': multiply(invert(tool_pose), base_pose),
'surface_from_object': multiply(invert(surface_pose), object_pose),
'base_from_object': multiply(invert(base_pose), object_pose),
})
print('Success! | {} / {} [{:.3f}]'.format(
len(entries), args.num_samples, elapsed_time(start_time)))
if has_gui():
wait_for_user()
#visualize_database(tool_from_base_list)
if not entries:
safe_remove(path)
return None
# Assuming the kitchen is fixed but the objects might be open world
data = {
'date': date,
'robot_name': robot_name, # get_name | get_body_name | get_base_name | world.robot_name
'base_link': get_link_name(world.robot, world.base_link),
'tool_link': get_link_name(world.robot, world.tool_link),
'kitchen_name': get_body_name(world.kitchen),
'surface_name': surface_name,
'object_name': object_name,
'grasp_type': grasp_type,
'entries': entries,
'failures': failures,
'successes': len(entries),
}
write_json(path, data)
print('Saved', path)
return data
def solve_serialized(robots,
obstacles,
node_points,
element_bodies,
ground_nodes,
layer_from_n,
trajectories=[],
post_process=False,
collisions=True,
disable=False,
max_time=INF,
**kwargs):
start_time = time.time()
saver = WorldSaver()
elements = set(element_bodies)
elements_from_layers = compute_elements_from_layer(elements, layer_from_n)
layers = sorted(elements_from_layers.keys())
print('Layers:', layers)
full_plan = []
makespan = 0.
removed = set()
for layer in reversed(layers):
print(SEPARATOR)
print('Layer: {}'.format(layer))
saver.restore()
remaining = elements_from_layers[layer]
printed = elements - remaining - removed
draw_model(remaining, node_points, ground_nodes, color=GREEN)
draw_model(printed, node_points, ground_nodes, color=RED)
problem = get_pddlstream(robots,
obstacles,
node_points,
element_bodies,
ground_nodes,
layer_from_n,
printed=printed,
removed=removed,
return_home=False,
trajectories=trajectories,
collisions=collisions,
disable=disable,
**kwargs)
layer_plan, certificate = solve_pddlstream(problem,
node_points,
element_bodies,
max_time=max_time -
elapsed_time(start_time))
remove_all_debug()
if layer_plan is None:
return None
if post_process:
print(SEPARATOR)
# Allows the planner to continue to check collisions
problem.init[:] = certificate.all_facts
#static_facts = extract_static_facts(layer_plan, ...)
#problem.init.extend(('Order',) + pair for pair in compute_total_orders(layer_plan))
for fact in [('print', ), ('move', )]:
if fact in problem.init:
problem.init.remove(fact)
new_layer_plan, _ = solve_pddlstream(problem,
node_points,
element_bodies,
planner=POSTPROCESS_PLANNER,
max_time=max_time -
elapsed_time(start_time))
if (new_layer_plan
is not None) and (compute_duration(new_layer_plan) <
compute_duration(layer_plan)):
layer_plan = new_layer_plan
user_input('{:.3f}->{:.3f}'.format(
compute_duration(layer_plan),
compute_duration(new_layer_plan)))
# TODO: replan in a cost sensitive way
layer_plan = apply_start(layer_plan, makespan)
duration = compute_duration(layer_plan)
makespan += duration
print(
'\nLength: {} | Start: {:.3f} | End: {:.3f} | Duration: {:.3f} | Makespan: {:.3f}'
.format(len(layer_plan), compute_start(layer_plan),
compute_end(layer_plan), duration, makespan))
full_plan.extend(layer_plan)
removed.update(remaining)
print(SEPARATOR)
print_plan(full_plan)
return full_plan, None
def regression(robot,
obstacles,
element_bodies,
extrusion_path,
partial_orders=[],
heuristic='z',
max_time=INF,
max_memory=INF,
backtrack_limit=INF,
revisit=False,
stiffness_attempts=1,
collisions=True,
stiffness=True,
motions=True,
lazy=LAZY,
checker=None,
**kwargs):
# Focused has the benefit of reusing prior work
# Greedy has the benefit of conditioning on previous choices
# TODO: max branching factor
# TODO: be more careful when near the end
# TODO: max time spent evaluating successors (less expensive when few left)
# TODO: tree rollouts
# TODO: best-first search with a minimizing path distance cost
# TODO: immediately select if becomes more stable
# TODO: focus branching factor on most stable regions
start_time = time.time()
initial_conf = get_configuration(robot)
initial_position = get_tool_position(robot)
element_from_id, node_points, ground_nodes = load_extrusion(extrusion_path)
id_from_element = get_id_from_element(element_from_id)
all_elements = frozenset(element_bodies)
ground_elements = get_ground_elements(all_elements, ground_nodes)
if stiffness and (checker is None):
checker = create_stiffness_checker(extrusion_path, verbose=False)
print_gen_fn = get_print_gen_fn(robot,
obstacles,
node_points,
element_bodies,
ground_nodes,
precompute_collisions=False,
max_directions=MAX_DIRECTIONS,
max_attempts=MAX_ATTEMPTS,
collisions=collisions,
**kwargs)
heuristic_fn = get_heuristic_fn(robot,
extrusion_path,
heuristic,
checker=checker,
forward=False)
queue = []
outgoing_from_element = outgoing_from_edges(partial_orders)
def add_successors(printed, position, conf):
only_ground = printed <= ground_elements
num_remaining = len(printed) - 1
#assert 0 <= num_remaining
for element in randomize(printed):
if not (outgoing_from_element[element] & printed) and implies(
is_ground(element, ground_nodes), only_ground):
for directed in get_directions(element):
visits = 0
bias = heuristic_fn(printed, directed, position, conf)
priority = (num_remaining, bias, random.random())
heapq.heappush(queue,
(visits, priority, printed, directed, conf))
final_conf = initial_conf # TODO: allow choice of final config
final_position = initial_position
final_printed = all_elements
visited = {final_printed: Node(None, None)}
if check_connected(ground_nodes, final_printed) and \
(not stiffness or test_stiffness(extrusion_path, element_from_id, final_printed, checker=checker)):
add_successors(final_printed, final_position, final_conf)
# if has_gui():
# sequence = sorted(final_printed, key=lambda e: heuristic_fn(final_printed, e, conf=None), reverse=True)
# remove_all_debug()
# draw_ordered(sequence, node_points)
# wait_for_user()
plan = None
min_remaining = len(all_elements)
num_evaluated = max_backtrack = extrusion_failures = transit_failures = stiffness_failures = 0
while queue and (elapsed_time(start_time) <
max_time) and check_memory(): #max_memory):
visits, priority, printed, directed, current_conf = heapq.heappop(
queue)
element = get_undirected(all_elements, directed)
num_remaining = len(printed)
backtrack = num_remaining - min_remaining
max_backtrack = max(max_backtrack, backtrack)
if backtrack_limit < backtrack:
break # continue
num_evaluated += 1
print(
'Iteration: {} | Best: {} | Printed: {} | Element: {} | Index: {} | Time: {:.3f}'
.format(num_evaluated, min_remaining, len(printed), element,
id_from_element[element], elapsed_time(start_time)))
next_printed = printed - {element}
next_nodes = compute_printed_nodes(ground_nodes, next_printed)
#draw_action(node_points, next_printed, element)
#if 3 < backtrack + 1:
# remove_all_debug()
# set_renderer(enable=True)
# draw_model(next_printed, node_points, ground_nodes)
# wait_for_user()
node1, node2 = directed
if (next_printed
in visited) or (node1
not in next_nodes) or not check_connected(
ground_nodes, next_printed):
continue
# TODO: stiffness plan lazily here possibly with reuse
if stiffness and not test_stiffness(
extrusion_path, element_from_id, next_printed,
checker=checker):
stiffness_failures += 1
continue
# TODO: stronger condition for this procedure
# if plan_stiffness(extrusion_path, element_from_id, node_points, ground_nodes, next_printed,
# checker=checker, max_backtrack=0) is None:
# # TODO: cache and reuse prior stiffness plans
# print('Failed stiffness plan') # TODO: require just a short horizon
# continue
if revisit:
heapq.heappush(
queue, (visits + 1, priority, printed, directed, current_conf))
command, = next(print_gen_fn(node1, element, extruded=next_printed),
(None, ))
if command is None:
extrusion_failures += 1
continue
if motions and not lazy:
motion_traj = compute_motion(
robot,
obstacles,
element_bodies,
printed,
command.end_conf,
current_conf,
collisions=collisions,
max_time=max_time -
elapsed_time(start_time)) # TODO: smooth=...)
if motion_traj is None:
transit_failures += 1
continue
command.trajectories.append(motion_traj)
if num_remaining < min_remaining:
min_remaining = num_remaining
#print('New best: {}'.format(num_remaining))
#if has_gui():
# # TODO: change link transparency
# remove_all_debug()
# draw_model(next_printed, node_points, ground_nodes)
# wait_for_duration(0.5)
visited[next_printed] = Node(
command, printed) # TODO: be careful when multiple trajs
if not next_printed:
min_remaining = 0
commands = retrace_commands(visited, next_printed, reverse=True)
if OPTIMIZE:
commands = optimize_commands(robot,
obstacles,
element_bodies,
extrusion_path,
initial_conf,
commands,
motions=motions,
collisions=collisions)
plan = flatten_commands(commands)
if motions and not lazy:
motion_traj = compute_motion(robot,
obstacles,
element_bodies,
frozenset(),
initial_conf,
plan[0].start_conf,
collisions=collisions,
max_time=max_time -
elapsed_time(start_time))
if motion_traj is None:
plan = None
transit_failures += 1
else:
plan.insert(0, motion_traj)
if motions and lazy:
plan = compute_motions(robot,
obstacles,
element_bodies,
initial_conf,
plan,
collisions=collisions,
max_time=max_time -
elapsed_time(start_time))
break
# if plan is not None:
# break
add_successors(next_printed, node_points[node1], command.start_conf)
#del checker
data = {
#'memory': get_memory_in_kb(), # May need to update instead
'num_evaluated': num_evaluated,
'min_remaining': min_remaining,
'max_backtrack': max_backtrack,
'stiffness_failures': stiffness_failures,
'extrusion_failures': extrusion_failures,
'transit_failures': transit_failures,
}
return plan, data
def main():
parser = argparse.ArgumentParser()
parser.add_argument('paths', nargs='*', help='Paths to the data.')
#parser.add_argument('-a', '--active', type=int, default=0, # None
# help='The number of active samples to collect')
parser.add_argument('-l', '--learner', default=None,
help='Path to the learner that should be used')
parser.add_argument('-n', '--num_trials', type=int, default=100,
help='The number of samples to collect')
parser.add_argument('-s', '--save', action='store_true',
help='Whether to save the learners')
parser.add_argument('-r', '--num_rounds', type=int, default=1,
help='The number of rounds to collect')
#parser.add_argument('-t', '--num_train', type=int, default=None,
# help='The size of the training set')
args = parser.parse_args()
# TODO: be careful that paging isn't altering the data
# TODO: don't penalize if the learner identifies that it can't make a good prediction
# TODO: use a different set of randomized parameters for train and test
include_none = False
serial = is_darwin()
#training_sizes = inclusive_range(50, 500, 25)
#training_sizes = inclusive_range(25, 100, 5)
#training_sizes = inclusive_range(25, 100, 5)
training_sizes = inclusive_range(10, 50, 5)
#training_sizes = inclusive_range(100, 1000, 100)
#training_sizes = [20]
#training_sizes = [1500]
#kernels = ['RBF', 'Matern52', 'MLP']
kernels = ['MLP']
#hyperparameters = [None]
#hyperparameters = [True]
hyperparameters = [True, None] # None,
query_type = BEST # BEST | CONFIDENT | REJECTION | ACTIVE # type of query used to evaluate the learner
is_adaptive = False
max_test = 50 #
#alphas = np.linspace(0.0, 0.9, num=5, endpoint=True)
alphas = [0.0, .8, .9, .99]
#alphas = [None] # Use the default (i.e. GP parameters)
use_vars = [True]
binary = False
split = UNIFORM # BALANCED
# Omitting failed labels is okay because they will never be executed
algorithms = []
#algorithms += [(Algorithm(BATCH_GP, kernel=kernel, hyperparameters=hype, use_var=use_var), [num_train])
# for num_train, kernel, hype, use_var in product(training_sizes, kernels, hyperparameters, use_vars)]
algorithms += [(Algorithm(STRADDLE_GP, kernel, hype, use_var), training_sizes)
for kernel, hype, use_var in product(kernels, hyperparameters, use_vars)]
#algorithms += [(Algorithm(rf_model, p_explore=None, use_var=use_var), [num_train])
# for rf_model, num_train, use_var in product(RF_MODELS, training_sizes, use_vars)]
#algorithms += [(Algorithm(nn_model, p_explore=None), [num_train])
# for nn_model, num_train in product(NN_MODELS, training_sizes)]
#algorithms += [(Algorithm(RANDOM), None), (Algorithm(DESIGNED), None)]
print('Algorithms:', algorithms)
print('Split:', split)
trials_per_round = sum(1 if train_sizes is None else
(train_sizes[-1] - train_sizes[0] + len(train_sizes))
for _, train_sizes in algorithms)
num_experiments = args.num_rounds*trials_per_round
date_name = datetime.datetime.now().strftime(DATE_FORMAT)
size_str = '[{},{}]'.format(training_sizes[0], training_sizes[-1])
#size_str = '-'.join(map(str, training_sizes))
experiments_name = '{}_r={}_t={}_n={}'.format(date_name, args.num_rounds, size_str, args.num_trials) #'19-08-09_21-44-58_r=5_t=[10,150]_n=1'#
#experiments_name = 't={}'.format(args.num_rounds)
# TODO: could include OS and username if desired
domain = load_data(args.paths)
print()
print(domain)
X, Y, W = domain.get_data(include_none=include_none)
print('Total number of examples:', len(X))
if binary:
# NN can fit perfectly when binary
# Binary seems to be outperforming w/o
Y = threshold_scores(Y)
max_train = len(X) - max_test #min(max([0] + [active_sizes[0] for _, active_sizes in algorithms
# if active_sizes is not None]), len(X))
#parameters = {
# 'include None': include_none,
# 'binary': binary,
# 'split': split,
#}
print('Name:', experiments_name)
print('Experiments:', num_experiments)
print('Max train:', max_train)
print('Include None:', include_none)
print('Examples: n={}, d={}'.format(*X.shape))
print('Binary:', binary)
print('Estimated hours:', num_experiments * SEC_PER_EXPERIMENT / HOURS_TO_SECS)
user_input('Begin?')
# TODO: residual learning for sim to real transfer
# TODO: can always be conservative and add sim negative examples
# TODO: combine all data to write in one folder
data_dir = os.path.join(DATA_DIRECTORY, domain.name) # EXPERIMENT_DIRECTORY
experiments_dir = os.path.join(data_dir, experiments_name)
mkdir(experiments_dir)
start_time = time.time()
experiments = []
for round_idx in range(args.num_rounds):
round_dir = os.path.join(data_dir, experiments_name, str(round_idx))
mkdir(round_dir)
seed = hash(time.time())
train_test_file = os.path.join(round_dir, 'data.pk3')
if not os.path.exists(train_test_file):
X_train, Y_train, X_test, Y_test = split_data(X, Y, split, max_train)
X_test, Y_test = X_test[:max_test], Y_test[:max_test]
write_pickle(train_test_file, (X_train, Y_train, X_test, Y_test))
else:
X_train, Y_train, X_test, Y_test = read_pickle(train_test_file)
print('Train examples:', X_train.shape)
print('Test examples:', X_test.shape)
# TODO: need to be super careful when running with multiple contexts
for algorithm, active_sizes in algorithms:
# active_sizes = [first #trainingdata selected from X_train, #active exploration + #trainingdata]
print(SEPARATOR)
print('Round: {} | {} | Seed: {} | Sizes: {}'.format(round_idx, algorithm, seed, active_sizes))
# TODO: allow keyboard interrupt
if active_sizes is None:
learner = algorithm.name
active_size = None
train_confusion = None
experiments.append(evaluate_learner(domain, seed, train_confusion, X_test, Y_test, algorithm, learner,
active_size, args.num_trials, alphas,
serial))
else:
# [10 20 25] take first 10 samples from X_train to train the model, 10 samples chosen actively
# sequentially + evaluate model, 5 samples chosen actively sequentially + evaluate model
# Could always keep around all the examples and retrain
# TODO: segfaults when this runs in parallel
# TODO: may be able to retrain in parallel if I set OPENBLAS_NUM_THREADS
learner_prior_nx = 0
'''
if algorithm.hyperparameters:
if domain.skill == 'pour':
learner_file = '/Users/ziw/ltamp_pr2/data/pour_19-06-13_00-59-21/19-08-09_19-30-01_r=10_t=[50,400]_n=1/{}/gp_active_mlp_true_true.pk3'.format(
round_idx)
elif domain.skill == 'scoop':
learner_file = '/Users/ziw/ltamp_pr2/data/scoop_19-06-10_20-16-59_top-diameter/19-08-09_19-34-56_r=10_t=[50,400]_n=1/{}/gp_active_mlp_true_true.pk3'.format(
round_idx)
learner = read_pickle(learner_file)
learner_prior_nx = learner.nx
learner.retrain(newx=X_train[:active_sizes[0]], newy=Y_train[:active_sizes[0], None])
else:
'''
learner, train_confusion = create_learner(domain, X_train, Y_train, split, algorithm,
num_train=active_sizes[0], query_type=query_type,
is_adaptive=is_adaptive)
if algorithm.name == STRADDLE_GP:
X_select, Y_select = X_train[active_sizes[0]:], Y_train[active_sizes[0]:]
for active_size in active_sizes:
num_active = active_size - learner.nx + learner_prior_nx# learner.nx is len(learner.xx)
print('\nRound: {} | {} | Seed: {} | Size: {} | Active: {}'.format(
round_idx, algorithm, seed, active_size, num_active))
if algorithm.name == STRADDLE_GP:
X_select, Y_select = active_learning_discrete(learner, num_active, X_select, Y_select)
#if args.save:
save_learner(round_dir, learner)
experiments.append(evaluate_learner(domain, seed, None, X_test, Y_test,
algorithm, learner,
active_size, args.num_trials, alphas,
serial))
save_experiments(experiments_dir, experiments)
print(SEPARATOR)
if experiments:
save_experiments(experiments_dir, experiments)
plot_experiments(domain, experiments_name, experiments_dir, experiments,
include_none=False)
#include_none=include_none)
print('Experiments:', experiments_dir)
print('Total experiments:', len(experiments))
print('Total hours:', elapsed_time(start_time) / HOURS_TO_SECS)
def solve_collision_free(door,
obstacle,
max_iterations=100,
step_size=math.radians(5),
min_distance=2e-2,
draw=True):
joints = get_movable_joints(door)
door_link = child_link_from_joint(joints[-1])
# print(get_com_pose(door, door_link))
# print(get_link_inertial_pose(door, door_link))
# print(get_link_pose(door, door_link))
# draw_pose(get_com_pose(door, door_link))
handles = []
success = False
start_time = time.time()
for iteration in range(max_iterations):
current_conf = np.array(get_joint_positions(door, joints))
collision_infos = get_closest_points(door,
obstacle,
link1=door_link,
max_distance=min_distance)
if not collision_infos:
success = True
break
collision_infos = sorted(collision_infos,
key=lambda info: info.contactDistance)
collision_infos = collision_infos[:1] # TODO: average all these
if draw:
for collision_info in collision_infos:
handles.extend(draw_collision_info(collision_info))
wait_if_gui()
[collision_info] = collision_infos[:1]
distance = collision_info.contactDistance
print(
'Iteration: {} | Collisions: {} | Distance: {:.3f} | Time: {:.3f}'.
format(iteration, len(collision_infos), distance,
elapsed_time(start_time)))
if distance >= min_distance:
success = True
break
# TODO: convergence or decay in step size
direction = step_size * get_unit_vector(
collision_info.contactNormalOnB) # B->A (already normalized)
contact_point = collision_info.positionOnA
#com_pose = get_com_pose(door, door_link) # TODO: be careful here
com_pose = get_link_pose(door, door_link)
local_point = tform_point(invert(com_pose), contact_point)
#local_point = unit_point()
translate, rotate = compute_jacobian(door,
door_link,
point=local_point)
delta_conf = np.array([
np.dot(translate[mj], direction) # + np.dot(rotate[mj], direction)
for mj in movable_from_joints(door, joints)
])
new_conf = current_conf + delta_conf
if violates_limits(door, joints, new_conf):
break
set_joint_positions(door, joints, new_conf)
if draw:
wait_if_gui()
remove_handles(handles)
print('Success: {} | Iteration: {} | Time: {:.3f}'.format(
success, iteration, elapsed_time(start_time)))
#quit()
return success
def main(num_iterations=10):
# The URDF loader seems robust to package:// and slightly wrong relative paths?
connect(use_gui=True)
add_data_path()
plane = p.loadURDF("plane.urdf")
side = 0.05
block = create_box(w=side, l=side, h=side, color=RED)
start_time = time.time()
with LockRenderer():
with HideOutput():
# TODO: MOVO must be loaded last
robot = load_model(MOVO_URDF, fixed_base=True)
#set_all_color(robot, color=MOVO_COLOR)
assign_link_colors(robot)
base_joints = joints_from_names(robot, BASE_JOINTS)
draw_base_limits((get_min_limits(
robot, base_joints), get_max_limits(robot, base_joints)),
z=1e-2)
print('Load time: {:.3f}'.format(elapsed_time(start_time)))
dump_body(robot)
#print(get_colliding(robot))
#for arm in ARMS:
# test_close_gripper(robot, arm)
#test_grasps(robot, block)
arm = RIGHT
tool_link = link_from_name(robot, TOOL_LINK.format(arm))
#joint_names = HEAD_JOINTS
#joints = joints_from_names(robot, joint_names)
joints = base_joints + get_arm_joints(robot, arm)
#joints = get_movable_joints(robot)
print('Joints:', get_joint_names(robot, joints))
ik_info = MOVO_INFOS[arm]
print_ik_warning(ik_info)
ik_joints = get_ik_joints(robot, ik_info, tool_link)
#fixed_joints = []
fixed_joints = ik_joints[:1]
#fixed_joints = ik_joints
wait_if_gui('Start?')
sample_fn = get_sample_fn(robot, joints)
handles = []
for i in range(num_iterations):
conf = sample_fn()
print('Iteration: {}/{} | Conf: {}'.format(i + 1, num_iterations,
np.array(conf)))
set_joint_positions(robot, joints, conf)
tool_pose = get_link_pose(robot, tool_link)
remove_handles(handles)
handles = draw_pose(tool_pose)
wait_if_gui()
#conf = next(ikfast_inverse_kinematics(robot, MOVO_INFOS[arm], tool_link, tool_pose,
# fixed_joints=fixed_joints, max_time=0.1), None)
#if conf is not None:
# set_joint_positions(robot, ik_joints, conf)
#wait_if_gui()
test_retraction(robot,
ik_info,
tool_link,
fixed_joints=fixed_joints,
max_time=0.05,
max_candidates=100)
disconnect()
