def main():
# save either optimized or random trajectory parameters to filename
if args.filename:
traj_file = args.filename
else:
traj_file = config['urdf'] + '.trajectory.npz'
if config['optimizeTrajectory']:
# find trajectory params by optimization
old_sim = config['simulateTorques']
config['simulateTorques'] = True
model = Model(config, config['urdf'])
if config['useStaticTrajectories']:
old_gravity = config['identifyGravityParamsOnly']
idf = Identification(config, config['urdf'], config['urdf_real'], measurements_files=None,
regressor_file=None, validation_file=None)
trajectoryOptimizer = PostureOptimizer(config, idf, model, simulation_func=simulateTrajectory, world=args.world)
config['identifyGravityParamsOnly'] = old_gravity
else:
idf = Identification(config, config['urdf'], urdf_file_real=None, measurements_files=None,
regressor_file=None, validation_file=None)
trajectoryOptimizer = TrajectoryOptimizer(config, idf, model, simulation_func=simulateTrajectory, world=args.world)
trajectory = trajectoryOptimizer.optimizeTrajectory()
config['simulateTorques'] = old_sim
else:
# use some random params
print("no optimized trajectory found, generating random one")
trajectory = PulsedTrajectory(config['num_dofs'], use_deg=config['useDeg']).initWithRandomParams()
print("a {}".format([t_a.tolist() for t_a in trajectory.a]))
print("b {}".format([t_b.tolist() for t_b in trajectory.b]))
print("q {}".format(trajectory.q.tolist()))
print("nf {}".format(trajectory.nf.tolist()))
print("wf {}".format(trajectory.w_f_global))
print("Saving found trajectory to {}".format(traj_file))
if config['useStaticTrajectories']:
# always saved with rad angles
np.savez(traj_file, static=True, angles=trajectory.angles)
else:
# TODO: remove degrees option
np.savez(traj_file, use_deg=trajectory.use_deg, static=False, a=trajectory.a, b=trajectory.b,
q=trajectory.q, nf=trajectory.nf, wf=trajectory.w_f_global)
config = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)
import iDynTree
iDynTree.init_helpers()
iDynTree.init_numpy_helpers()
dynComp = iDynTree.DynamicsComputations()
dynComp.loadRobotModelFromFile(args.model)
world_gravity = iDynTree.SpatialAcc.fromList([0, 0, -9.81, 0, 0, 0])
n_dof = dynComp.getNrOfDegreesOfFreedom()
config['num_dofs'] = n_dof
config['urdf'] = args.model
g_model = Model(config,
args.model,
regressor_file=None,
regressor_init=False)
linkNames = g_model.linkNames
# get bounding boxes for model
from identification.helpers import URDFHelpers, ParamHelpers
paramHelpers = ParamHelpers(None, config)
urdfHelpers = URDFHelpers(paramHelpers, None, config)
link_cuboid_hulls = {} # type: Dict[str, Tuple[List, List, List]]
for i in range(len(linkNames)):
link_name = linkNames[i]
box, pos, rot = urdfHelpers.getBoundingBox(input_urdf=args.model,
old_com=[0, 0, 0],
link_name=link_name,
scaling=False)
def simulateTrajectory(config, trajectory, model=None, measurements=None):
# generate data arrays for simulation and regressor building
old_sim = config['simulateTorques']
config['simulateTorques'] = True
if not model:
model = Model(config, config['model'])
data = Data(config)
trajectory_data = {}
trajectory_data['target_positions'] = []
trajectory_data['target_velocities'] = []
trajectory_data['target_accelerations'] = []
trajectory_data['torques'] = []
trajectory_data['times'] = []
freq=200.0
for t in range(0, int(trajectory.getPeriodLength()*freq)):
trajectory.setTime(t/freq)
q = [trajectory.getAngle(d) for d in range(config['N_DOFS'])]
q = np.array(q)
if config['useDeg']:
q = np.deg2rad(q)
trajectory_data['target_positions'].append(q)
qdot = [trajectory.getVelocity(d) for d in range(config['N_DOFS'])]
qdot = np.array(qdot)
if config['useDeg']:
qdot = np.deg2rad(qdot)
trajectory_data['target_velocities'].append(qdot)
qddot = [trajectory.getAcceleration(d) for d in range(config['N_DOFS'])]
qddot = np.array(qddot)
if config['useDeg']:
qddot = np.deg2rad(qddot)
trajectory_data['target_accelerations'].append(qddot)
trajectory_data['times'].append(t/freq)
trajectory_data['torques'].append(np.zeros(config['N_DOFS']))
num_samples = len(trajectory_data['times'])
trajectory_data['target_positions'] = np.array(trajectory_data['target_positions'])
trajectory_data['positions'] = trajectory_data['target_positions']
trajectory_data['target_velocities'] = np.array(trajectory_data['target_velocities'])
trajectory_data['velocities'] = trajectory_data['target_velocities']
trajectory_data['target_accelerations'] = np.array(trajectory_data['target_accelerations'])
trajectory_data['accelerations'] = trajectory_data['target_accelerations']
trajectory_data['torques'] = np.array(trajectory_data['torques'])
trajectory_data['times'] = np.array(trajectory_data['times'])
trajectory_data['measured_frequency'] = freq
trajectory_data['base_velocity'] = np.zeros( (num_samples, 6) )
trajectory_data['base_acceleration'] = np.zeros( (num_samples, 6) )
trajectory_data['base_rpy'] = np.zeros( (num_samples, 3) )
trajectory_data['contacts'] = np.array({'dummy_sim': np.zeros( num_samples )})
if measurements:
trajectory_data['positions'] = measurements['Q']
trajectory_data['velocities'] = measurements['V']
trajectory_data['accelerations'] = measurements['Vdot']
trajectory_data['measured_frequency'] = measurements['measured_frequency']
old_skip = config['skip_samples']
config['skip_samples'] = 0
old_offset = config['start_offset']
config['start_offset'] = 0
data.init_from_data(trajectory_data)
model.computeRegressors(data) #TODO: this needlessly also computes regressors in addition to simulation
config['skip_samples'] = old_skip
config['start_offset'] = old_offset
config['simulateTorques'] = old_sim
return trajectory_data, data, model
out['base_velocity'] = np.hstack( (out['IMUlinVel'], out['IMUrotVel']) )
out['base_acceleration'] = np.hstack( (out['IMUlinAcc'], out['IMUrotAcc']) )
out['contacts'] = np.array({'r_leg_ft': out['FTright']})
out['base_rpy'] = out['IMUrpy']
#simulate with iDynTree if we're using gazebo data
if not is_hw:
#use all data
old_skip = config['skip_samples']
config['skip_samples'] = 0
old_offset = config['start_offset']
config['start_offset'] = 0
old_sim = config['simulateTorques']
config['simulateTorques'] = 1
data.init_from_data(out)
model = Model(config, config['model'])
model.computeRegressors(data)
out['torques'] = out['torques_raw'] = model.data.samples['torques']
config['skip_samples'] = old_skip
config['start_offset'] = old_offset
config['simulateTorques'] = old_sim
np.savez(args.outfile, positions=out['positions'], positions_raw=out['positions'],
velocities=out['velocities'], velocities_raw=out['velocities'],
accelerations=out['accelerations'], torques=out['torques'],
torques_raw=out['torques_raw'], base_velocity=out['base_velocity'],
base_acceleration=out['base_acceleration'], base_rpy=out['base_rpy'],
contacts=out['contacts'], times=out['times'], frequency=out['frequency'])
print("Saved csv data as {}".format(args.outfile))
print("Samples: {}, Time: {}s, Frequency: {} Hz".format(out['times'].shape[0], out['times'][-1], out['frequency']))
Tau=out['torques'],
Tau_raw=out['torques_raw'],
T=out['times'],
Fs=out['frequency'])
#simulate with iDynTree if we're using gazebo data
if is_gazebo:
#use all data
old_skip = config['skipSamples']
config['skipSamples'] = 0
old_offset = config['startOffset']
config['startOffset'] = 0
old_sim = config['simulateTorques']
config['simulateTorques'] = 1
data.init_from_data(out)
model = Model(config, config['model'], args.regressor)
if config['verbose']:
print('simulating torques for motion data')
model.computeRegressors(data)
out['torques'] = out['torques_raw'] = model.data.samples['torques']
config['skipSamples'] = old_skip
config['startOffset'] = old_offset
config['simulateTorques'] = old_sim
if config['floatingBase']:
np.savez(args.outfile,
positions=out['positions'],
positions_raw=out['positions'],
target_positions=out['target_positions'],
velocities=out['velocities'],
velocities_raw=out['velocities'],
def __init__(self, opt, urdf_file, urdf_file_real, measurements_files, regressor_file, validation_file):
# type: (Dict, str, str, str, str, str) -> None
self.opt = opt
# some additional options (experiments)
# in order ot get regressor and base equations, use basis projection matrix. Otherwise use
# permutation from QR directly (Gautier/Sousa method)
self.opt['useBasisProjection'] = 0
# in case projection is used, orthogonalize the basis matrix (SDP estimation seem to work
# more stable that way)
self.opt['orthogonalizeBasis'] = 1
# add regularization term to SDP identification that minimized CAD distance for non-identifiable params
self.opt['useRegressorRegularization'] = 1
self.opt['regularizationFactor'] = 1000.0 #proportion of distance term
# if using fixed base dynamics, remove first link that is the fixed base which should completely
# not be identifiable and not be part of equations (as it does not move)
self.opt['deleteFixedBase'] = 1
# end additional config flags
# load model description and initialize
self.model = Model(self.opt, urdf_file, regressor_file)
# load measurements
self.data = Data(self.opt)
if measurements_files:
self.data.init_from_files(measurements_files)
self.paramHelpers = helpers.ParamHelpers(self.model, self.opt)
self.urdfHelpers = helpers.URDFHelpers(self.paramHelpers, self.model, self.opt)
self.sdp = sdp.SDP(self)
if self.opt['constrainUsingNL']:
from identification.nlopt import NLOPT
self.nlopt = NLOPT(self)
self.tauEstimated = None # type: np._ArrayLike
self.res_error = 100 # last residual error in percent
self.urdf_file_real = urdf_file_real
if self.urdf_file_real:
dc = iDynTree.DynamicsRegressorGenerator()
if not dc.loadRobotAndSensorsModelFromFile(urdf_file_real):
sys.exit()
tmp = iDynTree.VectorDynSize(self.model.num_model_params)
#set regressor, otherwise getModelParameters segfaults
dc.loadRegressorStructureFromString(self.model.regrXml)
dc.getModelParameters(tmp)
self.xStdReal = tmp.toNumPy()
#add some zeros for friction
self.xStdReal = np.concatenate((self.xStdReal, np.zeros(self.model.num_all_params-self.model.num_model_params)))
if self.opt['identifyFriction']:
self.paramHelpers.addFrictionFromURDF(self.model, self.urdf_file_real, self.xStdReal)
self.validation_file = validation_file
progress_inst = helpers.Progress(opt)
self.progress = progress_inst.progress
class Identification(object):
def __init__(self, opt, urdf_file, urdf_file_real, measurements_files, regressor_file, validation_file):
# type: (Dict, str, str, str, str, str) -> None
self.opt = opt
# some additional options (experiments)
# in order ot get regressor and base equations, use basis projection matrix. Otherwise use
# permutation from QR directly (Gautier/Sousa method)
self.opt['useBasisProjection'] = 0
# in case projection is used, orthogonalize the basis matrix (SDP estimation seem to work
# more stable that way)
self.opt['orthogonalizeBasis'] = 1
# add regularization term to SDP identification that minimized CAD distance for non-identifiable params
self.opt['useRegressorRegularization'] = 1
self.opt['regularizationFactor'] = 1000.0 #proportion of distance term
# if using fixed base dynamics, remove first link that is the fixed base which should completely
# not be identifiable and not be part of equations (as it does not move)
self.opt['deleteFixedBase'] = 1
# end additional config flags
# load model description and initialize
self.model = Model(self.opt, urdf_file, regressor_file)
# load measurements
self.data = Data(self.opt)
if measurements_files:
self.data.init_from_files(measurements_files)
self.paramHelpers = helpers.ParamHelpers(self.model, self.opt)
self.urdfHelpers = helpers.URDFHelpers(self.paramHelpers, self.model, self.opt)
self.sdp = sdp.SDP(self)
if self.opt['constrainUsingNL']:
from identification.nlopt import NLOPT
self.nlopt = NLOPT(self)
self.tauEstimated = None # type: np._ArrayLike
self.res_error = 100 # last residual error in percent
self.urdf_file_real = urdf_file_real
if self.urdf_file_real:
dc = iDynTree.DynamicsRegressorGenerator()
if not dc.loadRobotAndSensorsModelFromFile(urdf_file_real):
sys.exit()
tmp = iDynTree.VectorDynSize(self.model.num_model_params)
#set regressor, otherwise getModelParameters segfaults
dc.loadRegressorStructureFromString(self.model.regrXml)
dc.getModelParameters(tmp)
self.xStdReal = tmp.toNumPy()
#add some zeros for friction
self.xStdReal = np.concatenate((self.xStdReal, np.zeros(self.model.num_all_params-self.model.num_model_params)))
if self.opt['identifyFriction']:
self.paramHelpers.addFrictionFromURDF(self.model, self.urdf_file_real, self.xStdReal)
self.validation_file = validation_file
progress_inst = helpers.Progress(opt)
self.progress = progress_inst.progress
def estimateRegressorTorques(self, estimateWith=None, print_stats=False):
# type: (str, bool) -> None
""" get torque estimations using regressor, prepare for plotting """
if not estimateWith:
# use global parameter choice if none is given specifically
estimateWith = self.opt['estimateWith']
# estimate torques with idyntree regressor and different params
if estimateWith == 'urdf':
tauEst = np.dot(self.model.YStd, self.model.xStdModel[self.model.identified_params])
elif estimateWith == 'base_essential':
tauEst = np.dot(self.model.YBase, self.xBase_essential)
elif estimateWith == 'base':
tauEst = np.dot(self.model.YBase, self.model.xBase)
elif estimateWith in ['std', 'std_direct']:
tauEst = np.dot(self.model.YStd, self.model.xStd)
else:
print("unknown type of parameters: {}".format(self.opt['estimateWith']))
if self.opt['floatingBase']:
fb = 6
else:
fb = 0
if self.opt['floatingBase']:
# the base forces are expressed in the base frame for the regressor, so transform them
# to world frame (inverse dynamics use world frame)
'''
pos = iDynTree.Position.Zero()
tau_2dim = tauEst.reshape((self.data.num_used_samples, self.model.num_dofs+fb))
for i in range(self.data.num_used_samples):
idx = i*(self.opt['skipSamples'])+i
rpy = self.data.samples['base_rpy'][idx]
rot = iDynTree.Rotation.RPY(rpy[0], rpy[1], rpy[2])
world_T_base = iDynTree.Transform(rot, pos).inverse()
to_world = world_T_base.getRotation().toNumPy()
tau_2dim[i, :3] = to_world.dot(tau_2dim[i, :3])
tau_2dim[i, 3:6] = to_world.dot(tau_2dim[i, 3:6])
tauEst = tau_2dim.flatten()
'''
if self.opt['addContacts']:
tauEst += self.model.contactForcesSum
self.tauEstimated = np.reshape(tauEst, (self.data.num_used_samples, self.model.num_dofs + fb))
self.base_error = np.mean(sla.norm(self.model.tauMeasured - self.tauEstimated, axis=1))
# give some data statistics
if print_stats and (self.opt['verbose'] or self.opt['showErrorHistogram'] == 1):
error_per_joint = np.mean(self.model.tauMeasured - self.tauEstimated, axis=1)
#how gaussian is the error of the data vs estimation?
#http://stats.stackexchange.com/questions/62291/can-one-measure-the-degree-of-empirical-data-being-gaussian
if self.opt['verbose'] >= 2:
'''
W, p = stats.shapiro(error)
if p > 0.05:
print("error is normal distributed")
else:
print("error is not normal distributed (p={})".format(p))
print("W: {} (> 0.999 isn't too far from normality)".format(W))
'''
k2, p = stats.mstats.normaltest(error_per_joint)
if p > 0.05:
print("error is normal distributed")
else:
print("error is not normal distributed (p={})".format(p))
print("k2: {} (the closer it is to 0, the closer to normal distributed)".format(k2))
if self.opt['showErrorHistogram'] == 1:
plt.hist(error_per_joint, 50)
plt.title("error histogram")
plt.draw()
plt.show()
# don't show again if we come here later
self.opt['showErrorHistogram'] = 2
# reshape torques into one column per DOF for plotting (NUM_SAMPLES*num_dofsx1) -> (NUM_SAMPLESxnum_dofs)
if estimateWith == 'urdf':
self.tauAPriori = self.tauEstimated
def estimateValidationTorques(self):
""" calculate torques of trajectory from validation measurements and identified params """
# TODO: don't duplicate simulation code
# TODO: get identified params directly into idyntree (new KinDynComputations class does not
# have inverse dynamics yet, so we have to go over a new urdf file for now)
import os
v_data = np.load(self.validation_file)
dynComp = iDynTree.DynamicsComputations()
if self.opt['estimateWith'] == 'urdf':
params = self.model.xStdModel
else:
params = self.model.xStd
outfile = self.model.urdf_file + '.tmp.urdf'
self.urdfHelpers.replaceParamsInURDF(input_urdf=self.model.urdf_file,
output_urdf=outfile,
new_params=params)
if self.opt['useRBDL']:
import rbdl
self.model.rbdlModel = rbdl.loadModel(outfile,
floating_base=self.opt['floatingBase'],
verbose=False)
self.model.rbdlModel.gravity = np.array(self.model.gravity)
else:
dynComp.loadRobotModelFromFile(outfile)
os.remove(outfile)
old_skip = self.opt['skipSamples']
self.opt['skipSamples'] = 8
self.tauEstimatedValidation = None # type: np._ArrayLike
for m_idx in self.progress(range(0, v_data['positions'].shape[0], self.opt['skipSamples'] + 1)):
if self.opt['useRBDL']:
torques = self.model.simulateDynamicsRBDL(v_data, m_idx, None, params)
else:
torques = self.model.simulateDynamicsIDynTree(v_data, m_idx, dynComp, params)
if self.tauEstimatedValidation is None:
self.tauEstimatedValidation = torques
else:
self.tauEstimatedValidation = np.vstack((self.tauEstimatedValidation, torques))
if self.opt['skipSamples'] > 0:
self.tauMeasuredValidation = v_data['torques'][::self.opt['skipSamples'] + 1]
self.Tv = v_data['times'][::self.opt['skipSamples'] + 1]
else:
self.tauMeasuredValidation = v_data['torques']
self.Tv = v_data['times']
# add simulated base forces also to measurements
if self.opt['floatingBase']:
self.tauMeasuredValidation = \
np.concatenate((self.tauEstimatedValidation[:, :6], self.tauMeasuredValidation), axis=1)
#TODO: add contact forces to estimation, so far validation is only correct for fixed-base!
print(Fore.RED+'No proper validation for floating base yet!'+Fore.RESET)
self.opt['skipSamples'] = old_skip
self.val_error = sla.norm(self.tauEstimatedValidation - self.tauMeasuredValidation) \
* 100 / sla.norm(self.tauMeasuredValidation)
print("Relative validation error: {}%".format(self.val_error))
self.val_residual = np.mean(sla.norm(self.tauEstimatedValidation-self.tauMeasuredValidation, axis=1))
print("Absolute validation error: {} Nm".format(self.val_residual))
torque_limits = []
for joint in self.model.jointNames:
torque_limits.append(self.model.limits[joint]['torque'])
self.val_nrms = helpers.getNRMSE(self.tauMeasuredValidation, self.tauEstimatedValidation, limits=torque_limits)
print("NRMS validation error: {}%".format(self.val_nrms))
def getBaseParamsFromParamError(self):
# type: () -> None
self.model.xBase += self.model.xBaseModel # both param vecs link relative linearized
if self.opt['useEssentialParams']:
self.xBase_essential[self.baseEssentialIdx] += self.model.xBaseModel[self.baseEssentialIdx]
def findStdFromBaseParameters(self):
# type: () -> None
'''find std parameter from base parameters (simply projection method)'''
# Note: assumes that xBase is still in error form if using a priori
# i.e. don't call after getBaseParamsFromParamError
# project back to standard parameters
if self.opt['useBasisProjection']:
self.model.xStd = self.model.B.dot(self.model.xBase)
else:
self.model.xStd = la.pinv(self.model.K).dot(self.model.xBase)
# get estimated parameters from estimated error (add a priori knowledge)
if self.opt['useAPriori']:
self.model.xStd += self.model.xStdModel[self.model.identified_params]
def getStdDevForParams(self):
# type: () -> (np._ArrayLike[float])
# this might not be working correctly
if self.opt['useAPriori']:
tauDiff = self.model.tauMeasured - self.tauEstimated
else:
tauDiff = self.tauEstimated
if self.opt['floatingBase']: fb = 6
else: fb = 0
# get relative standard deviation of measurement and modeling error \sigma_{rho}^2
r = self.data.num_used_samples * (self.model.num_dofs + fb)
rho = np.square(sla.norm(tauDiff))
sigma_rho = rho / (r - self.model.num_base_params)
# get standard deviation \sigma_{x} (of the estimated parameter vector x)
C_xx = sigma_rho * (sla.pinv(np.dot(self.model.YBase.T, self.model.YBase)))
sigma_x = np.diag(C_xx)
# get relative standard deviation
p_sigma_x = np.sqrt(sigma_x)
for i in range(0, p_sigma_x.size):
if self.model.xBase[i] != 0:
p_sigma_x[i] /= np.abs(self.model.xBase[i])
return p_sigma_x
def findBaseEssentialParameters(self):
"""
iteratively get essential parameters from previously identified base parameters.
(goal is to get similar influence of all parameters, i.e. decrease condition number by throwing
out parameters that are too sensitive to errors. The remaining params should be estimated with
similar accuracy)
based on Pham, 1991; Gautier, 2013 and Jubien, 2014
"""
with helpers.Timer() as t:
# use mean least squares (actually median least abs) to determine when the error
# introduced by model reduction gets too large
use_error_criterion = 0
# keep current values
xBase_orig = self.model.xBase.copy()
YBase_orig = self.model.YBase.copy()
# count how many params were canceled
b_c = 0
# list of param indices to keep the original indices when deleting columns
base_idx = list(range(0, self.model.num_base_params))
not_essential_idx = list() # type: List[int]
ratio = 0
# get initial errors of estimation
self.estimateRegressorTorques('base')
if not self.opt['useAPriori']:
tauDiff = self.model.tauMeasured - self.tauEstimated
else:
tauDiff = self.tauEstimated
def error_func(tauDiff):
#rho = tauDiff
rho = np.mean(tauDiff, axis=1)
#rho = np.square(la.norm(tauDiff, axis=1))
return rho
error_start = error_func(tauDiff)
if self.opt['verbose']:
W, p = stats.shapiro(error_start)
#k2, p = stats.normaltest(error_start, axis=0)
if np.mean(p) > 0.05:
print("error is normal distributed")
else:
print("error is not normal distributed (p={})".format(p))
if not self.opt['useAPriori']:
pham_percent_start = sla.norm(tauDiff) * 100 / sla.norm(self.tauEstimated)
else:
pham_percent_start = sla.norm(tauDiff) * 100 / sla.norm(self.model.tauMeasured)
print("starting percentual error {}".format(pham_percent_start))
#rho_start = np.square(sla.norm(tauDiff))
p_sigma_x = np.array([0])
has_run_once = 0
# start removing non-essential parameters
while 1:
# get new torque estimation to calc error norm (new estimation with updated parameters)
self.estimateRegressorTorques('base')
prev_p_sigma_x = p_sigma_x
p_sigma_x = self.getStdDevForParams()
print("{} params|".format(self.model.num_base_params - b_c), end=' ')
ratio = np.max(p_sigma_x) / np.min(p_sigma_x)
print("min-max ratio of relative stddevs: {},".format(ratio), end=' ')
print("cond(YBase):{},".format(la.cond(self.model.YBase)), end=' ')
if not self.opt['useAPriori']:
tauDiff = self.model.tauMeasured - self.tauEstimated
else:
tauDiff = self.tauEstimated
pham_percent = sla.norm(tauDiff) * 100 / sla.norm(self.model.tauMeasured)
error_increase_pham = pham_percent_start - pham_percent
print("error delta {}".format(error_increase_pham))
# while loop condition moved to here
# TODO: consider to only stop when under ratio and
# if error is to large at that point, advise to get more/better data
if ratio < 30:
break
if use_error_criterion and error_increase_pham > 3.5:
break
if has_run_once and self.opt['showEssentialSteps']:
# put some values into global variable for output
self.baseNonEssentialIdx = not_essential_idx
self.baseEssentialIdx = [x for x in range(0, self.model.num_base_params) if x not in not_essential_idx]
self.num_essential_params = len(self.baseEssentialIdx)
self.xBase_essential = np.zeros_like(xBase_orig)
# take current xBase with reduced parameters as essentials to display
self.xBase_essential[self.baseEssentialIdx] = self.model.xBase
self.p_sigma_x = p_sigma_x
old_showStd = self.opt['showStandardParams']
old_showBase = self.opt['showBaseParams']
self.opt['showStandardParams'] = 0
self.opt['showBaseParams'] = 1
oc = OutputConsole(self)
oc.render(self)
self.opt['showStandardParams'] = old_showStd
self.opt['showBaseParams'] = old_showBase
print(base_idx, np.argmax(p_sigma_x))
print(self.baseNonEssentialIdx)
input("Press return...")
else:
has_run_once = 1
# cancel the parameter with largest deviation
param_idx = cast(int, np.argmax(p_sigma_x))
# get its index among the base params (otherwise it doesnt take deletion into account)
param_base_idx = base_idx[param_idx]
if param_base_idx not in not_essential_idx:
not_essential_idx.append(param_base_idx)
self.prev_xBase = self.model.xBase.copy()
self.model.xBase = np.delete(self.model.xBase, param_idx, 0)
base_idx = np.delete(base_idx, param_idx, 0)
self.model.YBase = np.delete(self.model.YBase, param_idx, 1)
# re-estimate parameters with reduced regressor
self.identifyBaseParameters()
b_c += 1
not_essential_idx.pop()
print("essential rel stddevs: {}".format(prev_p_sigma_x))
self.p_sigma_x = prev_p_sigma_x
# get indices of the essential base params
self.baseNonEssentialIdx = not_essential_idx
self.baseEssentialIdx = [x for x in range(0, self.model.num_base_params) if x not in not_essential_idx]
self.num_essential_params = len(self.baseEssentialIdx)
# leave previous base params and regressor unchanged
self.xBase_essential = np.zeros_like(xBase_orig)
self.xBase_essential[self.baseEssentialIdx] = self.prev_xBase
self.model.YBase = YBase_orig
self.model.xBase = xBase_orig
print("Got {} essential parameters".format(self.num_essential_params))
if self.opt['showTiming']:
print("Getting base essential parameters took %.03f sec." % t.interval)
def findStdFromBaseEssParameters(self):
""" Find essential standard parameters from previously determined base essential parameters. """
# get the choice of indices into the std params of the independent columns.
# Of those, only select the std parameters that are essential
self.stdEssentialIdx = self.model.independent_cols[self.baseEssentialIdx]
# intuitively, also the dependent columns should be essential as the linear combination
# is used to identify and calc the error
useCADWeighting = 0 # usually produces exact same result, but might be good for some tests
if self.opt['useDependents']:
# also get the ones that are linearly dependent on them -> base params
dependents = [] # type: List[int]
#to_delete = []
for i in range(0, self.model.base_deps.shape[0]):
if i in self.baseEssentialIdx:
for s in self.model.base_deps[i].free_symbols:
idx = self.model.param_syms.index(s)
if idx not in dependents:
dependents.append(idx)
#print self.stdEssentialIdx
#print len(dependents)
print(dependents)
self.stdEssentialIdx = np.concatenate((self.stdEssentialIdx, dependents))
#np.delete(self.stdEssentialIdx, to_delete, 0)
# remove mass params if present
#if self.opt['dontIdentifyMasses']:
# ps = list(range(0, self.model.num_identified_params, 10))
# self.stdEssentialIdx = np.fromiter((x for x in self.stdEssentialIdx if x not in ps), int)
self.stdNonEssentialIdx = [x for x in range(0, self.model.num_identified_params) if x not in self.stdEssentialIdx]
# get \hat{x_e}, set zeros for non-essential params
if self.opt['useDependents'] or useCADWeighting:
# we don't really know what the weights are if we have more std essential than base
# essentials, so use CAD/previous params for weighting
self.xStdEssential = self.model.xStdModel.copy()
# set essential but zero cad values to small values that are in possible range of those parameters
# so something can be estimated
#self.xStdEssential[np.where(self.xStdEssential == 0)[0]] = .1
idx = 0
for p in self.xStdEssential:
if p == 0:
v = 0.1
p_start = idx // 10 * 10
if idx % 10 in [1,2,3]: # com value
v = cast(float, np.mean(self.model.xStdModel[p_start + 1:p_start + 4]) * 0.1)
elif idx % 10 in [4,5,6,7,8,9]: # inertia value
inertia_range = np.array([4,5,6,7,8,9])+p_start
v = cast(float, np.mean(self.model.xStdModel[np.where(self.model.xStdModel[inertia_range] != 0)[0]+p_start+4]) * 0.1)
if v == 0:
v = 0.1
self.xStdEssential[idx] = v
#print idx, idx % 10, v
idx += 1
if idx > self.model.num_model_params:
break
# cancel non-essential std params so they are not identified
self.xStdEssential[self.stdNonEssentialIdx] = 0
else:
# weighting using base essential params (like in Gautier, 2013)
self.xStdEssential = np.zeros_like(self.model.xStdModel)
#if self.opt['useAPriori']:
# self.xStdEssential[self.stdEssentialIdx] = self.xBase_essential[self.baseEssentialIdx] \
# + self.xBaseModel[self.baseEssentialIdx]
#else:
self.xStdEssential[self.stdEssentialIdx] = self.xBase_essential[self.baseEssentialIdx]
def identifyBaseParameters(self, YBase=None, tau=None, id_only=False):
# type: (np._ArrayLike, np._ArrayLike, bool) -> None
"""use previously computed regressors and identify base parameter vector using ordinary or
weighted least squares."""
if YBase is None:
YBase = self.model.YBase
if tau is None:
tau = self.model.tau
if self.opt['useBasisProjection']:
self.model.xBaseModel = self.model.xStdModel.dot(self.model.B)
else:
self.model.xBaseModel = self.model.K.dot(self.model.xStdModel[self.model.identified_params])
if self.urdf_file_real:
if self.opt['useBasisProjection']:
self.xBaseReal = np.dot(self.model.Binv, self.xStdReal[self.model.identified_params])
else:
self.xBaseReal = self.model.K.dot(self.xStdReal[self.model.identified_params])
# note: using pinv is only ok if low condition number, otherwise numerical issues can happen
# should always try to avoid inversion of ill-conditioned matrices if possible
# invert equation to get parameter vector from measurements and model + system state values
self.model.YBaseInv = la.pinv(YBase)
# identify using numpy least squares method (should be numerically more stable)
self.model.xBase = la.lstsq(YBase, tau)[0]
if self.opt['addContacts']:
self.model.xBase -= self.model.YBaseInv.dot(self.model.contactForcesSum)
"""
# using pseudoinverse
self.model.xBase = self.model.YBaseInv.dot(tau.T) - self.model.YBaseInv.dot(self.model.contactForcesSum)
# damped least squares
from scipy.sparse.linalg import lsqr
self.model.xBase = lsqr(YBase, tau, damp=10)[0] - self.model.YBaseInv.dot(self.model.contactForcesSum)
"""
# stop here if called recursively
if id_only:
return
if self.opt['showBaseParams'] or self.opt['verbose'] or self.opt['useRegressorRegularization']:
# get estimation once with previous ordinary LS solution parameters
self.estimateRegressorTorques('base', print_stats=True)
if 'selectingBlocks' not in self.opt or not self.opt['selectingBlocks']:
self.p_sigma_x = self.getStdDevForParams()
if self.opt['useWLS']:
"""
additionally do weighted least squares IDIM-WLS, cf. Zak, 1994, Gautier, 1997 and Khalil, 2007.
adds weighting with relative standard dev of estimation error on OLS base regressor and params.
(includes reducing effect of different units of parameters)
"""
# get estimation once with previous ordinary LS solution parameters
self.estimateRegressorTorques('base')
self.p_sigma_x = self.getStdDevForParams()
if self.opt['floatingBase']: fb = 6
else: fb = 0
r = self.data.num_used_samples*(self.model.num_dofs+fb)
'''
if self.opt['useAPriori']:
tauDiff = self.model.tauMeasured - self.tauEstimated
else:
tauDiff = self.tauEstimated
# get standard deviation of measurement and modeling error \sigma_{rho}^2
# for each joint subsystem (rho is assumed zero mean independent noise)
self.sigma_rho = np.square(sla.norm(tauDiff)) / (r-self.model.num_base_params)
'''
# repeat stddev values for each measurement block (n_joints * num_samples)
# along the diagonal of G
# G = np.diag(np.repeat(1/self.sigma_rho, self.num_used_samples))
#G = scipy.sparse.spdiags(np.tile(1/self.sigma_rho, self.num_used_samples), 0,
# self.num_dofs*self.num_used_samples, self.num_dofs*self.num_used_samples)
#G = scipy.sparse.spdiags(np.repeat(1/np.sqrt(self.sigma_rho), self.data.num_used_samples), 0, r, r)
G = scipy.sparse.spdiags(np.repeat(np.array([1/self.p_sigma_x]), self.data.num_used_samples), 0, r, r)
# weigh Y and tau with deviations
self.model.YBase = G.dot(self.model.YBase)
if self.opt['useAPriori']:
#if identifying parameter error, weigh full tau
self.model.tau = G.dot(self.model.torques_stack) - G.dot(self.model.torquesAP_stack)
else:
self.model.tau = G.dot(self.model.tau)
if self.opt['verbose']:
print("Condition number of WLS YBase: {}".format(la.cond(self.model.YBase)))
# get identified values using weighted matrices without weighing them again
self.identifyBaseParameters(self.model.YBase, tau, id_only=True)
def identifyStandardParametersDirect(self):
"""Identify standard parameters directly with non-singular standard regressor."""
with helpers.Timer() as t:
U, s, VH = la.svd(self.model.YStd, full_matrices=False)
nb = self.model.num_base_params
# get non-singular std regressor
V_1 = VH.T[:, 0:nb]
U_1 = U[:, 0:nb]
s_1 = np.diag(s[0:nb])
s_1_inv = la.inv(s_1)
W_st_pinv = V_1.dot(s_1_inv).dot(U_1.T)
W_st = la.pinv(W_st_pinv)
self.YStd_nonsing = W_st
#TODO: add contact forces
x_est = W_st_pinv.dot(self.model.tau)
if self.opt['useAPriori']:
self.model.xStd = self.model.xStdModel + x_est
else:
self.model.xStd = x_est
"""
st = self.model.num_identified_params
# non-singular YStd, called W_st in Gautier, 2013
self.YStdHat = self.YStd - U[:, nb:st].dot(np.diag(s[nb:st])).dot(V[:,nb:st].T)
self.YStdHatInv = la.pinv(self.YStdHat)
x_tmp = np.dot(self.YStdHatInv, self.model.tau)
if self.opt['useAPriori']:
self.model.xStd = self.model.xStdModel + x_tmp
else:
self.model.xStd = x_tmp
"""
if self.opt['showTiming']:
print("Identifying std parameters directly took %.03f sec." % t.interval)
def identifyStandardEssentialParameters(self):
"""Identify standard essential parameters directly with non-singular standard regressor."""
with helpers.Timer() as t:
# weighting with previously determined essential params
# calculates V_1e, U_1e etc. (Gautier, 2013)
Yst_e = self.model.YStd.dot(np.diag(self.xStdEssential)) # = W_st^e
Ue, se, VHe = sla.svd(Yst_e, full_matrices=False)
ne = self.num_essential_params # nr. of essential params among base params
V_1e = VHe.T[:, 0:ne]
U_1e = Ue[:, 0:ne]
s_1e_inv = sla.inv(np.diag(se[0:ne]))
W_st_e_pinv = np.diag(self.xStdEssential).dot(V_1e.dot(s_1e_inv).dot(U_1e.T))
#W_st_e = la.pinv(W_st_e_pinv)
#TODO: add contact forces
x_tmp = W_st_e_pinv.dot(self.model.tau)
if self.opt['useAPriori']:
self.model.xStd = self.model.xStdModel + x_tmp
else:
self.model.xStd = x_tmp
if self.opt['showTiming']:
print("Identifying %s std essential parameters took %.03f sec." % (len(self.stdEssentialIdx), t.interval))
def estimateParameters(self):
'''identify parameters using data and regressor (method depends on chosen options)'''
if not self.data.num_used_samples > self.model.num_identified_params*2 \
and 'selectingBlocks' in self.opt and not self.opt['selectingBlocks']:
print(Fore.RED+"not enough samples for identification!"+Fore.RESET)
if self.opt['startOffset'] > 0:
print("(startOffset is at {})".format(self.opt['startOffset']))
sys.exit(1)
if self.opt['verbose']:
print("computing standard regressor matrix for data samples")
self.model.computeRegressors(self.data)
if self.opt['verbose']:
print("estimating parameters using regressor")
if self.opt['useEssentialParams']:
self.identifyBaseParameters()
self.findBaseEssentialParameters()
if self.opt['useAPriori']:
self.getBaseParamsFromParamError()
self.findStdFromBaseEssParameters()
self.identifyStandardEssentialParameters()
else:
#need to identify OLS base params in any case
self.identifyBaseParameters()
if self.opt['constrainToConsistent']:
if self.opt['useAPriori']:
self.getBaseParamsFromParamError()
if self.opt['identifyClosestToCAD']:
# first estimate feasible base params, then find corresponding feasible std
# params while minimizing distance to CAD
self.sdp.initSDP_LMIs(self)
self.sdp.identifyFeasibleStandardParameters(self)
# get feasible base solution by projection
if self.opt['useBasisProjection']:
self.model.xBase = self.model.Binv.dot(self.model.xStd)
else:
self.model.xBase = self.model.K.dot(self.model.xStd)
print("Trying to find equal solution closer to a priori values")
if self.opt['constrainUsingNL']:
self.nlopt.identifyFeasibleStdFromFeasibleBase(self.model.xBase)
else:
self.sdp.findFeasibleStdFromFeasibleBase(self, self.model.xBase)
else:
self.sdp.initSDP_LMIs(self)
# directly estimate constrained std params, distance to CAD not minimized
if self.opt['estimateWith'] == 'std_direct':
#self.identifyStandardParametersDirect() #get std nonsingular regressor
self.sdp.identifyFeasibleStandardParametersDirect(self) #use with sdp
else:
if self.opt['constrainUsingNL']:
self.model.xStd = self.model.xStdModel.copy()
self.nlopt.identifyFeasibleStandardParameters()
else:
self.sdp.identifyFeasibleStandardParameters(self)
#self.sdp.identifyFeasibleBaseParameters(self)
#self.model.xStd = self.model.xBase.dot(self.model.K)
if self.opt['useBasisProjection']:
self.model.xBase = self.model.Binv.dot(self.model.xStd)
else:
self.model.xBase = self.model.K.dot(self.model.xStd)
# get OLS standard parameters (with a priori), then correct to feasible
#self.findStdFromBaseParameters()
#if self.opt['useAPriori']:
# self.getBaseParamsFromParamError()
# correct std solution to feasible if necessary (e.g. infeasible solution from
# unsuccessful optimization run)
"""
if not self.paramHelpers.isPhysicalConsistent(self.model.xStd) and not self.opt['constrainUsingNL']:
#get full LMIs again
self.opt['deleteFixedBase'] = 0
self.sdp.initSDP_LMIs(self, remove_nonid=False)
print("Correcting solution to feasible std (non-optimal)")
self.model.xStd = self.sdp.findFeasibleStdFromStd(self, self.model.xStd)
"""
else:
#identify with OLS only
#get standard params from estimated base param error
if self.opt['estimateWith'] == 'std_direct':
self.identifyStandardParametersDirect()
else:
self.findStdFromBaseParameters()
#only then go back to absolute base params
if self.opt['useAPriori']:
self.getBaseParamsFromParamError()
def plot(self, text=None):
# type: (str) -> None
"""Create state and torque plots."""
if self.opt['verbose']:
print('plotting')
rel_time = self.model.T-self.model.T[0]
if self.validation_file:
rel_vtime = self.Tv-self.Tv[0]
if self.opt['floatingBase']:
fb = 6
else:
fb = 0
if not self.opt['plotBaseDynamics'] or not self.opt['floatingBase']:
# get only data for joints (skipping base data if present)
tauMeasured = self.model.tauMeasured[:, fb:]
tauEstimated = self.tauEstimated[:, fb:]
tauAPriori = self.tauAPriori[:, fb:]
if self.validation_file:
tauEstimatedValidation = self.tauEstimatedValidation[:, fb:]
tauMeasuredValidation = self.tauMeasuredValidation[:, fb:]
torque_labels = self.model.jointNames
else:
# get all data for floating base
tauMeasured = self.model.tauMeasured
tauEstimated = self.tauEstimated
tauAPriori = self.tauAPriori
if self.validation_file:
tauEstimatedValidation = self.tauEstimatedValidation
tauMeasuredValidation = self.tauMeasuredValidation
torque_labels = self.model.baseNames + self.model.jointNames
if self.opt['plotPerJoint']:
datasets = []
# plot base dynamics
if self.opt['floatingBase']:
if self.opt['plotBaseDynamics']:
for i in range(6):
datasets.append({
'unified_scaling': False,
#'y_label': '$F {{ {} }}$ (Nm)'.format(i),
'y_label': 'Force (N)',
'labels': ['Measured', 'Identified'], 'contains_base': False,
'dataset': [{
'data': [np.vstack((tauMeasured[:,i], tauEstimated[:,i])).T],
'time': rel_time, 'title': torque_labels[i]}
]}
)
# add plots for each joint
for i in range(fb, self.model.num_dofs):
datasets.append({
'unified_scaling': False,
#'y_label': '$\\tau_{{ {} }}$ (Nm)'.format(i+1),
'y_label': 'Torque (Nm)',
'labels': ['Measured', 'Identified'], 'contains_base': False,
'dataset': [{
'data': [np.vstack((tauMeasured[:,i], tauEstimated[:,i])).T],
'time': rel_time, 'title': torque_labels[i]}
]}
)
if self.opt['plotPrioriTorques']:
#plot a priori torques
apriori = tauAPriori[:,i]
datasets[-1]['dataset'][0]['data'][0] = np.vstack((datasets[-1]['dataset'][0]['data'][0].T, apriori)).T
datasets[-1]['labels'].append('CAD')
if self.opt['plotErrors']:
#plot joint torque errors
e = tauMeasured[:,i] - tauEstimated[:,i]
datasets[-1]['dataset'][0]['data'][0] = np.vstack((datasets[-1]['dataset'][0]['data'][0].T, e)).T
datasets[-1]['labels'].append('Error M/E')
# positions per joint
for i in range(self.model.num_dofs):
datasets.append(
{'unified_scaling': False, 'y_label': 'rad', 'labels': ['Position'], 'dataset':
[{'data': [self.data.samples['positions'][0:self.model.sample_end:self.opt['skipSamples']+1, i],
#self.data.samples['target_positions'][0:self.model.sample_end:self.opt['skipSamples']+1, i]
],
'time': rel_time, 'title': self.model.jointNames[i]},
]
}
)
# vel and acc combined
datasets.append(
{'unified_scaling': False, 'y_label': 'rad/s (/s2)', 'labels': self.model.jointNames, 'dataset':
[{'data': [self.data.samples['velocities'][0:self.model.sample_end:self.opt['skipSamples']+1]],
'time': rel_time, 'title': 'Velocities'},
{'data': [self.data.samples['accelerations'][0:self.model.sample_end:self.opt['skipSamples']+1]],
'time': rel_time, 'title': 'Accelerations'},
]
}
)
else: #don't plot per joint
datasets = [
{'unified_scaling': True, 'y_label': 'Torque (Nm)', 'labels': torque_labels,
'contains_base': self.opt['floatingBase'] and self.opt['plotBaseDynamics'],
'dataset':
[{'data': [tauMeasured], 'time': rel_time, 'title': 'Measured Torques'},
{'data': [tauEstimated], 'time': rel_time, 'title': 'Estimation with identified Params'},
{'data': [tauAPriori], 'time': rel_time, 'title': 'Estimation with A priori Params'},
]
},
{'unified_scaling': True, 'y_label': 'Torque (Nm)', 'labels': torque_labels,
'contains_base': self.opt['floatingBase'] and self.opt['plotBaseDynamics'],
'dataset':
[{'data': [tauMeasured-tauEstimated], 'time': rel_time, 'title': 'Identified Estimation Error'},
{'data': [tauMeasured-tauAPriori], 'time': rel_time, 'title': 'A priori Estimation Error'},
]
},
{'unified_scaling': False, 'y_label': 'rad (/s, /s2)', 'labels': self.model.jointNames, 'dataset':
[{'data': [self.data.samples['positions'][0:self.model.sample_end:self.opt['skipSamples']+1]],
'time': rel_time, 'title': 'Positions'},
{'data': [self.data.samples['velocities'][0:self.model.sample_end:self.opt['skipSamples']+1]],
'time': rel_time, 'title': 'Velocities'},
{'data': [self.data.samples['accelerations'][0:self.model.sample_end:self.opt['skipSamples']+1]],
'time': rel_time, 'title': 'Accelerations'},
]
}
]
if 'positions_raw' in self.data.samples:
datasets[2]['dataset'][0]['data'].append(self.data.samples['positions_raw'][0:self.model.sample_end:self.opt['skipSamples']+1])
if 'velocities_raw' in self.data.samples:
datasets[2]['dataset'][1]['data'].append(self.data.samples['velocities_raw'][0:self.model.sample_end:self.opt['skipSamples']+1])
if self.validation_file:
datasets.append(
{'unified_scaling': True, 'y_label': 'Torque (Nm)', 'labels': torque_labels,
'contains_base': self.opt['floatingBase'] and self.opt['plotBaseDynamics'],
'dataset':
[#{'data': [self.tauMeasuredValidation],
# 'time': rel_vtime, 'title': 'Measured Validation'},
{'data': [tauEstimatedValidation],
'time': rel_vtime, 'title': 'Estimated Validation'},
{'data': [tauEstimatedValidation - tauMeasuredValidation],
'time': rel_vtime, 'title': 'Validation Error'}
]
}
)
if self.opt['outputModule'] == 'matplotlib':
from identification.output import OutputMatplotlib
output = OutputMatplotlib(datasets, text=text)
output.render(self)
else:
print('No known output module given. Not creating plots!')
def printMemUsage(self):
import humanize
total = 0
print("Memory usage:")
for v in self.__dict__:
if type(self.__dict__[v]).__module__ == np.__name__:
size = self.__dict__[v].nbytes
total += size
print("{}: {} ".format(v, (humanize.naturalsize(size, binary=True))), end=' ')
#TODO: extend for builtins
print("- total: {}".format(humanize.naturalsize(total, binary=True)))
def simulateTrajectory(config, trajectory, model=None, measurements=None):
# type: (Dict, Trajectory, Model, np._ArrayLike) -> Tuple[Dict, Data]
# generate data arrays for simulation and regressor building
old_sim = config['simulateTorques']
config['simulateTorques'] = True
if config['floatingBase']: fb = 6
else: fb = 0
if not model:
if 'urdf_real' in config and config['urdf_real']:
print('Simulating using "real" model parameters.')
urdf = config['urdf_real']
else:
urdf = config['urdf']
model = Model(config, urdf)
data = Data(config)
trajectory_data = {} # type: Dict[str, Union[List, np._ArrayLike]]
trajectory_data['target_positions'] = []
trajectory_data['target_velocities'] = []
trajectory_data['target_accelerations'] = []
trajectory_data['torques'] = []
trajectory_data['times'] = []
freq = config['excitationFrequency']
#f= open("trajectory3.txt","a+")
for t in range(0, int(trajectory.getPeriodLength() * freq)):
trajectory.setTime(t / freq)
q = np.array(
[trajectory.getAngle(d) for d in range(config['num_dofs'])])
if config['useDeg']:
q = np.deg2rad(q)
trajectory_data['target_positions'].append(q)
#f.write("%s\n" % q)
qdot = np.array(
[trajectory.getVelocity(d) for d in range(config['num_dofs'])])
if config['useDeg']:
qdot = np.deg2rad(qdot)
trajectory_data['target_velocities'].append(qdot)
qddot = np.array(
[trajectory.getAcceleration(d) for d in range(config['num_dofs'])])
if config['useDeg']:
qddot = np.deg2rad(qddot)
trajectory_data['target_accelerations'].append(qddot)
trajectory_data['times'].append(t / freq)
trajectory_data['torques'].append(np.zeros(config['num_dofs'] + fb))
#f.write("next iteration \n")
#f.close()
num_samples = len(trajectory_data['times'])
#convert lists to numpy arrays
trajectory_data['target_positions'] = np.array(
trajectory_data['target_positions'])
trajectory_data['positions'] = trajectory_data['target_positions']
trajectory_data['target_velocities'] = np.array(
trajectory_data['target_velocities'])
trajectory_data['velocities'] = trajectory_data['target_velocities']
trajectory_data['target_accelerations'] = np.array(
trajectory_data['target_accelerations'])
trajectory_data['accelerations'] = trajectory_data['target_accelerations']
trajectory_data['torques'] = np.array(trajectory_data['torques'])
trajectory_data['times'] = np.array(trajectory_data['times'])
trajectory_data['measured_frequency'] = freq
trajectory_data['base_velocity'] = np.zeros((num_samples, 6))
trajectory_data['base_acceleration'] = np.zeros((num_samples, 6))
trajectory_data['base_rpy'] = np.zeros((num_samples, 3))
# add static contact force
contacts = 1
if config['floatingBase'] and contacts:
contactFrame = 'contact_ft'
# get base acceleration that results from acceleration at contact frame
#contact_wrench = np.zeros(6)
#contact_wrench[2] = 9.81 * 3.0 # -g * mass
contact_wrench = np.random.rand(6) * 10
trajectory_data['base_rpy'] = np.random.rand(
(3 * num_samples)).reshape((num_samples, 3)) * 0.5
"""
contact_wrench[2] = 9.81 # * 139.122814
len_contact = la.norm(contact_wrench[0:3])
# get vector from contact frame to robot center of mass
model_com = iDynTree.Position()
model_com = model.dynComp.getWorldTransform(contactFrame).inverse()*model.dynComp.getCenterOfMass()
model_com = model_com.toNumPy()
# rotate contact wrench to be in line with COM (so that base link is not rotationally accelerated)
contact_wrench[0:3] = (-model_com) / la.norm(model_com) * len_contact
# rotate base accordingly (ore rather the whole robot) so gravity is parallel to contact force
a_xz = np.array([0,9.81]) #xz of non-rotated contact force
b_xz = contact_wrench[[0,2]] #rotated contact force vec projected to xz
pitch = np.arccos( (a_xz.dot(b_xz))/(la.norm(a_xz)*la.norm(b_xz)) ) #angle in xz layer
a_yz = np.array([0,9.81]) #yz of non-rotated contact force
b_yz = contact_wrench[[1,2]] #rotated contact force vec projected to yz
roll = np.arccos( (a_yz.dot(b_yz))/(la.norm(a_yz)*la.norm(b_yz)) ) #angle in yz layer
yaw = 0
trajectory_data['base_rpy'][:] += np.array([roll, pitch, yaw])
"""
trajectory_data['contacts'] = np.array(
{contactFrame: np.tile(contact_wrench, (num_samples, 1))})
else:
#TODO: add proper simulated contacts (from e.g. gazebo) for floating-base
trajectory_data['contacts'] = np.array({})
if measurements:
trajectory_data['positions'] = measurements['Q']
trajectory_data['velocities'] = measurements['V']
trajectory_data['accelerations'] = measurements['Vdot']
trajectory_data['measured_frequency'] = measurements[
'measured_frequency']
old_skip = config['skipSamples']
config['skipSamples'] = 0
old_offset = config['startOffset']
config['startOffset'] = 0
data.init_from_data(trajectory_data)
model.computeRegressors(data)
trajectory_data['torques'][:, :] = data.samples['torques'][:, :]
'''
if config['floatingBase']:
# add force of contact to keep robot fixed in space (always accelerate exactly against gravity)
# floating base orientation has to be rotated so that accelerations resulting from hanging
# are zero, i.e. the vector COM - contact point is parallel to gravity.
if contacts:
# get jacobian of contact frame at current posture
dim = model.num_dofs+fb
jacobian = iDynTree.MatrixDynSize(6, dim)
model.dynComp.getFrameJacobian(contactFrame, jacobian)
jacobian = jacobian.toNumPy()
# get base link vel and acc and torques that result from contact force / acceleration
contacts_torq = np.zeros(dim)
contacts_torq = jacobian.T.dot(contact_wrench)
trajectory_data['base_acceleration'] += contacts_torq[0:6] # / 139.122
data.samples['base_acceleration'][:,:] = trajectory_data['base_acceleration'][:,:]
# simulate again with proper base acceleration
model.computeRegressors(data, only_simulate=True)
trajectory_data['torques'][:,:] = data.samples['torques'][:,:]
'''
config['skipSamples'] = old_skip
config['startOffset'] = old_offset
config['simulateTorques'] = old_sim
return trajectory_data, data