def __init__(self,
tracker: Kineverse6DEKFTracker,
model=None,
model_path=None,
urdf_param='/ekf_description_check'):
self.tracker = tracker
self.last_observation = {}
self.last_update = None
self.last_control = {s: 0.0 for s in self.tracker.controls}
self.str_controls = {str(s) for s in self.tracker.controls}
self.vis = ROSVisualizer('~vis', 'world')
if model is not None:
self.broadcaster = ModelTFBroadcaster_URDF(urdf_param, model_path,
model, Path('ekf'))
else:
self.broadcaster = None
self.pub_state = rospy.Publisher('~state_estimate',
ValueMapMsg,
queue_size=1,
tcp_nodelay=True)
self.sub_observations = rospy.Subscriber('~observations',
TransformStampedArrayMsg,
callback=self.cb_obs,
queue_size=1)
self.sub_controls = rospy.Subscriber('~controls',
ValueMapMsg,
callback=self.cb_controls,
queue_size=1)
def pr2_setup(km, pr2_path):
start_pose = {'l_elbow_flex_joint' : -2.1213,
'l_shoulder_lift_joint': 1.2963,
'l_wrist_flex_joint' : -1.16,
'l_shoulder_pan_joint': 0.7,
'r_shoulder_pan_joint': -1.0,
'r_shoulder_lift_joint': 0.9,
'r_upper_arm_roll_joint': -1.2,
'r_elbow_flex_joint' : -2.1213,
'r_wrist_flex_joint' : -1.05,
'r_forearm_roll_joint': 3.14,
'r_wrist_roll_joint': 0,
'torso_lift_joint' : 0.16825}
start_pose = {gm.Position(Path(f'{pr2_path}/{n}')): v for n, v in start_pose.items()}
joint_symbols = [j.position for j in km.get_data(f'{pr2_path}/joints').values()
if hasattr(j, 'position') and gm.is_symbol(j.position)]
robot_controlled_symbols = {gm.DiffSymbol(j) for j in joint_symbols} # if 'torso' not in str(j)}
blacklist = {gm.Velocity(Path(f'{pr2_path}/torso_lift_joint'))}
return joint_symbols, \
robot_controlled_symbols, \
start_pose, \
km.get_data(pr2_path).links['r_gripper_tool_frame'], \
blacklist
def __init__(self,
tracker: Kineverse6DQPTracker,
model=None,
model_path=None,
reference_frame='world',
urdf_param='/qp_description_check',
update_freq=30,
observation_alias=None):
self.tracker = tracker
self.last_observation = 0
self.last_update = 0
self.reference_frame = reference_frame
self.observation_aliases = {o: o for o in tracker.observation_names}
if observation_alias is not None:
for path, alias in observation_alias.items():
if path in self.observation_aliases:
self.observation_aliases[alias] = path
self.str_controls = {str(s) for s in self.tracker.get_controls()}
self.vis = ROSVisualizer('~vis', reference_frame)
if model is not None:
self.broadcaster = ModelTFBroadcaster_URDF(urdf_param, model_path,
model, Path('ekf'))
else:
self.broadcaster = None
self.tf_buffer = tf2_ros.Buffer()
self.listener = tf2_ros.TransformListener(self.tf_buffer)
self.pub_state = rospy.Publisher('~state_estimate',
ValueMapMsg,
queue_size=1,
tcp_nodelay=True)
self.sub_observations = rospy.Subscriber('~observations',
TransformStampedArrayMsg,
callback=self.cb_obs,
queue_size=1)
self.sub_controls = rospy.Subscriber('~controls',
ValueMapMsg,
callback=self.cb_controls,
queue_size=1)
self.timer = rospy.Timer(rospy.Duration(1 / update_freq),
self.cb_update)
if __name__ == '__main__':
rospy.init_node('iiwa_kinematic_sim')
rosparam_description = rospy.get_param('/robot_description', None)
if rosparam_description is None:
with open(
res_pkg_path(
'package://kineverse_experiment_world/urdf/iiwa_wsg_50.urdf'
), 'r') as f:
rosparam_description = f.read()
urdf = load_urdf_str(rosparam_description)
km = GeometryModel()
load_urdf(km, Path('iiwa'), urdf)
km.clean_structure()
km.dispatch_events()
sim = KineverseKinematicSim(km,
Path('iiwa'),
state_topic='/iiwa/joint_states')
sorted_names = sorted(sim.state_info.keys())
js_msg = JointStateMsg()
js_msg.name = sorted_names
def cb_pos_array_command(msg):
js_msg.header.stamp = rospy.Time.now()
js_msg.position = msg.data
vis = ROSBPBVisualizer('~vis', base_frame='world')
km = GeometryModel()
robot_type = rospy.get_param('~robot', 'pr2')
if robot_type.lower() == 'pr2':
robot_urdf = load_urdf_file('package://iai_pr2_description/robots/pr2_calibrated_with_ft2.xml')
elif robot_type.lower() == 'hsrb':
robot_urdf = load_urdf_file('package://hsr_description/robots/hsrb4s.obj.urdf')
elif robot_type.lower() == 'fetch':
robot_urdf = load_urdf_file('package://fetch_description/robots/fetch.urdf')
else:
print(f'Unknown robot {robot_type}')
exit(1)
robot_name = robot_urdf.name
robot_path = Path(robot_name)
load_urdf(km, robot_path, robot_urdf, Path('world'))
km.clean_structure()
model_name = load_model(km,
rospy.get_param('~model', 'nobilia'),
'world',
1.0, 0, 0.7, rospy.get_param('~yaw', 0.57))
km.clean_structure()
km.dispatch_events()
print('\n'.join(km.timeline_tags.keys()))
if rospy.get_param('~use_base', False):
if robot_type.lower() == 'fetch':
observations = rospy.get_param('~features', None)
num_samples = rospy.get_param('~samples', 5)
if type(observations) is not dict:
print(type(observations))
print(
'Required parameter ~features needs to be a dict mapping tracked model paths to observation names'
)
exit(1)
km = GeometryModel()
model_name = load_localized_model(km, model_path, reference_frame)
km.clean_structure()
km.dispatch_events()
model = km.get_data(model_name)
tracker = Kineverse6DQPTracker(km,
observations.keys(),
num_samples=num_samples)
node = ROSQPEManager(tracker,
model,
Path(model_name),
reference_frame,
observation_alias=observations)
while not rospy.is_shutdown():
rospy.sleep(0.1)
def main(create_figure=False,
vis_mode=False,
log_csv=True,
min_n_dof=1,
samples=300,
n_observations=25,
noise_lin=0.2,
noise_ang=30,
noise_steps=5):
wait_duration = rospy.Duration(0.1)
vis = ROSBPBVisualizer('ekf_vis', 'world') if vis_mode != 'none' else None
km = GeometryModel()
with open(res_pkg_path('package://iai_kitchen/urdf_obj/IAI_kitchen.urdf'),
'r') as urdf_file:
urdf_kitchen_str = urdf_file.read()
kitchen_model = urdf_filler(
URDF.from_xml_string(hacky_urdf_parser_fix(urdf_kitchen_str)))
load_urdf(km, Path('kitchen'), kitchen_model)
km.clean_structure()
km.dispatch_events()
kitchen = km.get_data('kitchen')
tracking_pools = []
for name, link in kitchen.links.items():
symbols = gm.free_symbols(link.pose)
if len(symbols) == 0:
continue
for x in range(len(tracking_pools)):
syms, l = tracking_pools[x]
if len(symbols.intersection(syms)
) != 0: # BAD ALGORITHM, DOES NOT CORRECTLY IDENTIFY SETS
tracking_pools[x] = (syms.union(symbols),
l + [(name, link.pose)])
break
else:
tracking_pools.append((symbols, [(name, link.pose)]))
# tracking_pools = [tracking_pools[7]]
# print('Identified {} tracking pools:\n{}'.format(len(tracking_pools), tracking_pools))
all_ekfs = [
EKFModel(dict(poses), km.get_constraints_by_symbols(symbols))
for symbols, poses in tracking_pools
] # np.eye(len(symbols)) * 0.001
print('Created {} EKF models'.format(len(all_ekfs)))
print('\n'.join(str(e) for e in all_ekfs))
# Sanity constraint
min_n_dof = min(min_n_dof, len(all_ekfs))
iteration_times = []
for u in range(min_n_dof, len(all_ekfs) + 1):
if rospy.is_shutdown():
break
ekfs = all_ekfs[:u]
observed_poses = {}
for ekf in ekfs:
for link_name, _ in ekf.takers:
observed_poses[link_name] = kitchen.links[link_name].pose
names, poses = zip(*sorted(observed_poses.items()))
state_symbols = union([gm.free_symbols(p) for p in poses])
ordered_state_vars = [
s for _, s in sorted((str(s), s) for s in state_symbols)
]
state_constraints = {}
for n, c in km.get_constraints_by_symbols(state_symbols).items():
if gm.is_symbol(c.expr):
s = gm.free_symbols(c.expr).pop()
fs = gm.free_symbols(c.lower).union(gm.free_symbols(c.upper))
if len(fs.difference({s})) == 0:
state_constraints[s] = (float(gm.subs(c.lower, {s: 0})),
float(gm.subs(c.upper, {s: 0})))
state_bounds = np.array([
state_constraints[s]
if s in state_constraints else [-np.pi * 0.5, np.pi * 0.5]
for s in ordered_state_vars
])
state_fn = gm.speed_up(gm.vstack(*poses), ordered_state_vars)
subworld = km.get_active_geometry(state_symbols)
# Generate observation noise
print('Generating R matrices...')
n_cov_obs = 400
full_log = []
dof_iters = []
# EXPERIMENT
for lin_std, ang_std in [(noise_lin, noise_ang * (np.pi / 180.0))]:
# zip(np.linspace(0, noise_lin, noise_steps),
# np.linspace(0, noise_ang * (np.pi / 180.0), noise_steps)):
if rospy.is_shutdown():
break
# INITIALIZE SENSOR MODEL
training_obs = []
state = np.random.uniform(state_bounds.T[0], state_bounds.T[1])
observations = state_fn.call2(state)
for _ in range(n_cov_obs):
noisy_obs = {}
for x, noise in enumerate([
t.dot(r) for t, r in zip(
random_normal_translation(len(poses), 0, lin_std),
random_rot_normal(len(poses), 0, ang_std))
]):
noisy_obs[names[x]] = observations[x * 4:x * 4 +
4, :4].dot(noise)
training_obs.append(noisy_obs)
for ekf in ekfs:
ekf.generate_R(training_obs)
# ekf.set_R(np.eye(len(ekf.ordered_vars)) * 0.1)
# Generate figure
gridsize = (4, samples)
plot_size = (4, 4)
fig = plt.figure(figsize=(gridsize[1] * plot_size[0], gridsize[0] *
plot_size[1])) if create_figure else None
gt_states = []
states = [[] for x in range(samples)]
variances = [[] for x in range(samples)]
e_obs = [[] for x in range(samples)]
print('Starting iterations')
for k in tqdm(range(samples)):
if rospy.is_shutdown():
break
state = np.random.uniform(state_bounds.T[0], state_bounds.T[1])
gt_states.append(state)
observations = state_fn.call2(state).copy()
gt_obs_d = {
n: observations[x * 4:x * 4 + 4, :4]
for x, n in enumerate(names)
}
subworld.update_world(dict(zip(ordered_state_vars, state)))
if vis_mode == 'iter' or vis_mode == 'io':
vis.begin_draw_cycle('gt', 'noise', 'estimate', 't_n',
't0')
vis.draw_world('gt', subworld, g=0, b=0)
vis.render('gt')
estimates = []
for ekf in ekfs:
particle = ekf.spawn_particle()
estimates.append(particle)
initial_state = dict(
sum([[(s, v) for s, v in zip(ekf.ordered_vars, e.state)]
for e, ekf in zip(estimates, ekfs)], []))
initial_state = np.array(
[initial_state[s] for s in ordered_state_vars])
if initial_state.min() < state_bounds.T[0].min(
) or initial_state.max() > state_bounds.T[1].max():
raise Exception(
'Estimate initialization is out of bounds: {}'.format(
np.vstack([initial_state, state_bounds.T]).T))
initial_delta = state - initial_state
for y in range(n_observations):
# Add noise to observations
noisy_obs = {}
for x, noise in enumerate([
t.dot(r) for t, r in zip(
random_normal_translation(
len(poses), 0, lin_std),
random_rot_normal(len(poses), 0, ang_std))
]):
noisy_obs[names[x]] = observations[x * 4:x * 4 +
4, :4].dot(noise)
if vis_mode in {'iter', 'iter-trail'} or (vis_mode == 'io'
and y == 0):
for n, t in noisy_obs.items():
subworld.named_objects[Path(
('kitchen', 'links', n))].np_transform = t
if vis_mode != 'iter-trail':
vis.begin_draw_cycle('noise')
vis.draw_world('noise', subworld, r=0, g=0, a=0.1)
vis.render('noise')
start_time = time()
for estimate, ekf in zip(estimates, ekfs):
if y > 0:
control = np.zeros(len(ekf.ordered_controls))
estimate.state, estimate.cov = ekf.predict(
estimate.state.flatten(), estimate.cov,
control)
obs_vector = ekf.gen_obs_vector(noisy_obs)
estimate.state, estimate.cov = ekf.update(
estimate.state, estimate.cov,
ekf.gen_obs_vector(noisy_obs))
if vis_mode in {'iter', 'iter-trail'}:
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
else:
obs_vector = ekf.gen_obs_vector(noisy_obs)
for _ in range(1):
h_prime = ekf.h_prime_fn.call2(estimate.state)
obs_delta = obs_vector.reshape(
(len(obs_vector), 1)) - ekf.h_fn.call2(
estimate.state)
estimate.state += (h_prime.T.dot(obs_delta) *
0.1).reshape(
estimate.state.shape)
if vis_mode in {'iter', 'io'}:
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
if vis_mode != 'none' and y == 0:
vis.draw_world('t0', subworld, b=0, a=1)
vis.render('t0')
elif vis_mode in {'iter', 'iter-trail'}:
if vis_mode != 'iter-trail':
vis.begin_draw_cycle('t_n')
vis.draw_world('t_n', subworld, b=0, a=1)
vis.render('t_n')
if log_csv or fig is not None:
e_state_d = dict(
sum([[(s, v)
for s, v in zip(ekf.ordered_vars, e.state)]
for e, ekf in zip(estimates, ekfs)], []))
covs = dict(
sum([[(s, v) for s, v in zip(
ekf.ordered_vars, np.sqrt(np.trace(e.cov)))]
for e, ekf in zip(estimates, ekfs)], []))
e_state = np.hstack([
e_state_d[s] for s in ordered_state_vars
]).reshape((len(e_state_d), ))
if log_csv:
full_log.append(
np.hstack(
([lin_std,
ang_std], state, e_state.flatten(),
np.array([
covs[s] for s in ordered_state_vars
]))))
if fig is not None:
e_obs[k].append(
np.array([
np.abs(
ekf.gen_obs_vector(gt_obs_d) -
ekf.h_fn.call2(e.state)).max()
for e, ekf in zip(estimates, ekfs)
]))
states[k].append(e_state)
variances[k].append(
np.array([covs[s]
for s in ordered_state_vars]))
else:
if vis_mode == 'io':
for estimate, ekf in zip(estimates, ekfs):
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
vis.draw_world('t_n', subworld, r=0, b=0, a=1)
vis.render('t_n')
dof_iters.append(time() - start_time)
if fig is not None:
axes = [
plt.subplot2grid(gridsize, (y, 0), colspan=1, rowspan=1)
for y in range(gridsize[0])
]
axes = np.array(
sum([[
plt.subplot2grid(gridsize, (y, x),
colspan=1,
rowspan=1,
sharey=axes[y])
for y in range(gridsize[0])
] for x in range(1, gridsize[1])], axes)).reshape(
(gridsize[1], gridsize[0]))
for x, (gt_s, state, variance, obs_delta,
(ax_s, ax_d, ax_o, ax_v)) in enumerate(
zip(gt_states, states, variances, e_obs, axes)):
for y in gt_s:
ax_s.axhline(y, xmin=0.97, xmax=1.02)
ax_s.set_title('State; Sample: {}'.format(x))
ax_d.set_title('Delta from GT; Sample: {}'.format(x))
ax_o.set_title('Max Delta in Obs; Sample: {}'.format(x))
ax_v.set_title('Standard Deviation; Sample: {}'.format(x))
ax_s.plot(state)
ax_d.plot(gt_s - np.vstack(state))
ax_o.plot(obs_delta)
ax_v.plot(variance)
ax_s.grid(True)
ax_d.grid(True)
ax_o.grid(True)
ax_v.grid(True)
fig.tight_layout()
plt.savefig(
res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker_{}_{}.png'
.format(lin_std, ang_std)))
iteration_times.append(dof_iters)
if log_csv:
df = pd.DataFrame(
columns=['lin_std', 'ang_std'] +
['gt_{}'.format(x) for x in range(len(state_symbols))] +
['ft_{}'.format(x) for x in range(len(state_symbols))] +
['var_{}'.format(x) for x in range(len(state_symbols))],
data=full_log)
df.to_csv(res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker.csv'
),
index=False)
df = pd.DataFrame(
columns=[str(x) for x in range(1,
len(iteration_times) + 1)],
data=np.vstack(iteration_times).T)
df.to_csv(res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker_performance.csv'
),
index=False)
visualizer.render('world')
def handle_sigint(*args):
dpg.stop_dearpygui()
if __name__ == '__main__':
rospy.init_node('nobilia_shelf_test')
km = GeometryModel()
vis = ROSBPBVisualizer('kineverse/nobilia/vis', base_frame='world')
origin_pose = gm.translation3(x_loc_sym, 0, 0)
debug = create_nobilia_shelf(km, Path('nobilia'), origin_pose)
km.clean_structure()
km.dispatch_events()
shelf = km.get_data('nobilia')
shelf_pos = shelf.joints['hinge'].position
world = km.get_active_geometry({shelf_pos
}.union(gm.free_symbols(origin_pose)))
params = debug.tuning_params
with dpg.window(label='Parameter settings'):
for s, p in debug.tuning_params.items():
#!/usr/bin/env python
import kineverse.gradients.common_math as cm
from kineverse.gradients.gradient_math import point3, vector3, translation3, frame3_axis_angle, Position
from kineverse.model.geometry_model import GeometryModel, RigidBody, Geometry, Path
from kineverse.operations.basic_operations import CreateComplexObject as OP_CCO, \
CreateSingleValue as OP_CSV
from kineverse.operations.special_kinematics import SetBallJoint as OP_SBJ
if __name__ == '__main__':
km = GeometryModel()
geom_head = Geometry(
Path('head'),
cm.eye(4),
'mesh',
mesh='package://kineverse_experiment_world/urdf/faucet_head.obj')
rb_head = RigidBody(Path('world'),
cm.eye(4),
geometry={0: geom_head},
collision={0: geom_head})
geom_base = Geometry(
Path('base'),
cm.eye(4),
'mesh',
mesh='package://kineverse_experiment_world/urdf/faucet_base.obj')
rb_base = RigidBody(Path('world'),
cm.eye(4),
geometry={0: geom_base},
from kineverse.motion.integrator import CommandIntegrator
from kineverse.visualization.bpb_visualizer import ROSBPBVisualizer
from kineverse.visualization.plotting import draw_recorders, split_recorders
from kineverse.visualization.trajectory_visualizer import TrajectoryVisualizer
from urdf_parser_py.urdf import URDF
if __name__ == '__main__':
rospy.init_node('microwave_sandbox')
micro_urdf = URDF.from_xml_file(
res_pkg_path('package://kineverse/urdf/microwave.urdf'))
km = EventModel()
#km = GeometryModel()
load_urdf(km, Path('microwave'), micro_urdf)
traj_vis = TrajectoryVisualizer(ROSBPBVisualizer('/bullet_test', '/map'))
traj_vis.add_articulated_object(micro_urdf, km.get_data('microwave'))
door_position = km.get_data('microwave/joints/door_joint').position
button_position = km.get_data('microwave/joints/button_joint').position
button_min = -0.02
door_velocity = get_diff_symbol(door_position)
door_accel = get_diff_symbol(door_velocity)
point_of_release = 0.8 * button_min
button_pushed = less_than(button_position, point_of_release)
condition = alg_and(less_than(door_position, 0.1), button_pushed)
spring_constraint = Constraint((2 * condition)**(-100 * door_position),
1e9, door_accel)
except KeyError as e:
print(
f'Failed to get nobilia localization params. Original error:\n{e}')
exit()
reference_frame = rospy.get_param('~reference_frame', 'world')
grab_from_tf = rospy.get_param('~grab_from_tf', False)
grab_rate = rospy.get_param('~grab_rate', 20.0)
shelf_location = gm.point3(location_x, location_y, location_z)
# origin_pose = gm.frame3_rpy(0, 0, 0, shelf_location)
origin_pose = gm.frame3_rpy(0, 0, location_yaw, shelf_location)
create_nobilia_shelf(km,
Path('nobilia'),
origin_pose,
parent_path=Path(reference_frame))
km.clean_structure()
km.dispatch_events()
shelf = km.get_data('nobilia')
tracker = Kineverse6DEKFTracker(
km,
# [Path(f'nobilia/markers/{name}') for name in shelf.markers.keys()], #
# [Path(f'nobilia/markers/body')],
{Path(f'nobilia/markers/top_panel'): 'obs_shelf_body'},
#{x: x for x in gm.free_symbols(origin_pose)},
noise_estimation_observations=20,
estimate_init_steps=1000)
) == '.xml':
urdf_model = load_urdf_file(description)
else:
if not rospy.has_param(description):
print(
f'Description is supposed to be located at "{description}" but that parameter does not exist'
)
urdf_model = load_urdf_str(rospy.get_param(description))
name = rospy.get_param('~name', urdf_model.name)
reference_frame = rospy.get_param('~reference_frame', 'world')
km = GeometryModel()
load_urdf(km,
Path(name),
urdf_model,
reference_frame,
joint_prefix='',
root_transform=root_transform)
km.clean_structure()
if special_base is not None:
if special_base == 'omni':
insert_omni_base(km, Path(name), urdf_model.get_root(),
reference_frame)
elif special_base == 'diff':
insert_diff_base(km, Path(name), urdf_model.get_root(),
reference_frame)
)
exit(1)
if len(root_transform) == 6:
root_transform = gm.frame3_rpy(root_transform[3],
root_transform[4],
root_transform[5],
root_transform[:3])
elif len(root_transform) == 7:
root_transform = gm.frame3_rpy(*root_transform)
else:
print(
'root_transform needs to encode transform eiter as xyzrpy, or as xyzqxqyqzqw'
)
exit(1)
km = GeometryModel()
create_nobilia_shelf(km, Path('nobilia'), root_transform)
km.clean_structure()
km.dispatch_events()
sim = KineverseKinematicSim(km,
Path('nobilia'),
'/nobilia_description',
command_type=ValueMapMsg)
print(f'Running kinematics sim for nobilia shelf')
while not rospy.is_shutdown():
rospy.sleep(1)