def generate_results(self, root_dir, args):
model = osim.Model()
body = osim.Body("b", 1, osim.Vec3(0), osim.Inertia(0))
model.addBody(body)
joint = osim.SliderJoint("j", model.getGround(), body)
joint.updCoordinate().setName("coord")
model.addJoint(joint)
damper = osim.SpringGeneralizedForce("coord")
damper.setViscosity(-1.0)
model.addForce(damper)
actu = osim.CoordinateActuator("coord")
model.addForce(actu)
model.finalizeConnections()
moco = osim.MocoStudy()
problem = moco.updProblem()
problem.setModel(model)
problem.setTimeBounds(0, 2)
problem.setStateInfo("/jointset/j/coord/value", [-10, 10], 0, 5)
problem.setStateInfo("/jointset/j/coord/speed", [-10, 10], 0, 2)
problem.setControlInfo("/forceset/coordinateactuator", [-50, 50])
problem.addGoal(osim.MocoControlGoal("effort", 0.5))
solver = moco.initCasADiSolver()
solver.set_num_mesh_intervals(50)
solution = moco.solve()
solution.write(self.solution_file % root_dir)
def predict(self, root_dir):
model = self.muscle_driven_model(root_dir)
moco = self.create_study(model)
problem = moco.updProblem()
problem.addGoal(osim.MocoControlGoal('effort'))
problem.addGoal(osim.MocoInitialActivationGoal('init_activation'))
problem.addGoal(osim.MocoFinalTimeGoal('time'))
solver = osim.MocoCasADiSolver.safeDownCast(moco.updSolver())
solver.resetProblem(problem)
guess = solver.createGuess()
N = guess.getNumTimes()
for muscle in model.getMuscles():
dgf = osim.DeGrooteFregly2016Muscle.safeDownCast(muscle)
if not dgf.get_ignore_tendon_compliance():
guess.setState(
'%s/normalized_tendon_force' % muscle.getAbsolutePathString(),
osim.createVectorLinspace(N, 0.1, 0.1))
guess.setState(
'%s/activation' % muscle.getAbsolutePathString(),
osim.createVectorLinspace(N, 0.05, 0.05))
guess.setControl(muscle.getAbsolutePathString(),
osim.createVectorLinspace(N, 0.05, 0.05))
solver.setGuess(guess)
solution = moco.solve()
solution.write(self.predict_solution_file % root_dir)
# moco.visualize(solution)
return solution
def solveMarkerTracking(markersRef, guess):
# Predict the optimal trajectory for a minimum time swing-up.
study = osim.MocoStudy()
study.setName("double_pendulum_track")
problem = study.updProblem()
# Model (dynamics).
problem.setModel(createDoublePendulumModel())
# Bounds.
# Arguments are name, [lower bound, upper bound],
# initial [lower bound, upper bound],
# final [lower bound, upper bound].
finalTime = markersRef.getMarkerTable().getIndependentColumn()[-1]
problem.setTimeBounds(0, finalTime)
problem.setStateInfo("/jointset/j0/q0/value", [-10, 10], 0)
problem.setStateInfo("/jointset/j0/q0/speed", [-50, 50], 0)
problem.setStateInfo("/jointset/j1/q1/value", [-10, 10], 0)
problem.setStateInfo("/jointset/j1/q1/speed", [-50, 50], 0)
problem.setControlInfo("/tau0", [-100, 100])
problem.setControlInfo("/tau1", [-100, 100])
# Cost: track provided marker data.
markerTracking = osim.MocoMarkerTrackingGoal()
markerTracking.setMarkersReference(markersRef)
problem.addGoal(markerTracking)
effort = osim.MocoControlGoal()
effort.setName("effort")
effort.setWeight(0.0001)
# problem.addGoal(effort)
# Configure the solver.
solver = study.initTropterSolver()
solver.set_num_mesh_intervals(50)
solver.set_verbosity(2)
solver.set_optim_solver("ipopt")
solver.set_optim_jacobian_approximation("exact")
solver.set_optim_hessian_approximation("exact")
solver.set_exact_hessian_block_sparsity_mode("dense")
solver.setGuess(guess)
# Save the problem to a setup file for reference.
study.printToXML("examplePredictAndTrack_track_markers.omoco")
# Solve the problem.
solution = study.solve()
solution.write("examplePredictAndTrack_track_markers_solution.sto")
if visualize:
study.visualize(solution)
return solution
def test_changing_costs(self):
# Changes to the costs are obeyed.
study = osim.MocoStudy()
problem = study.updProblem()
problem.setModel(createSlidingMassModel())
problem.setTimeBounds(0, [0, 10])
problem.setStateInfo("/slider/position/value", [0, 1], 0, 1)
problem.setStateInfo("/slider/position/speed", [-100, 100], 0, 0)
problem.updPhase().addGoal(osim.MocoFinalTimeGoal())
effort = osim.MocoControlGoal("effort")
problem.updPhase().addGoal(effort)
solver = study.initTropterSolver()
solver.set_transcription_scheme("trapezoidal")
finalTime0 = study.solve().getFinalTime()
# Change the weights of the costs.
effort.setWeight(0.1)
assert (study.solve().getFinalTime() < 0.8 * finalTime0)
def predict(self, run_time_stepping=False, exponent=2):
study = self.create_study()
problem = study.updProblem()
problem.setTimeBounds(0, [0.4, 0.8])
effort = osim.MocoControlGoal("effort")
effort.setExponent(exponent)
problem.addGoal(effort)
problem.addGoal(osim.MocoFinalTimeGoal("time", 0.01))
solution = study.solve()
# study.visualize(solution)
time_stepping = None
if run_time_stepping:
time_stepping = osim.simulateIterateWithTimeStepping(
solution, self.build_model())
return solution, time_stepping
def predict_assisted(self, root_dir):
model = self.assisted_model(root_dir)
moco = self.create_study(model)
problem = moco.updProblem()
problem.addGoal(osim.MocoControlGoal('effort'))
problem.addGoal(osim.MocoInitialActivationGoal('init_activation'))
problem.addGoal(osim.MocoFinalTimeGoal('time'))
problem.addParameter(
osim.MocoParameter('stiffness', '/forceset/spring',
'stiffness', osim.MocoBounds(0, 100)))
solver = osim.MocoCasADiSolver.safeDownCast(moco.updSolver())
solver.resetProblem(problem)
guess = solver.createGuess()
N = guess.getNumTimes()
for muscle in model.getMuscles():
dgf = osim.DeGrooteFregly2016Muscle.safeDownCast(muscle)
if not dgf.get_ignore_tendon_compliance():
guess.setState(
'%s/normalized_tendon_force' % muscle.getAbsolutePathString(),
osim.createVectorLinspace(N, 0.1, 0.1))
guess.setState(
'%s/activation' % muscle.getAbsolutePathString(),
osim.createVectorLinspace(N, 0.05, 0.05))
guess.setControl(muscle.getAbsolutePathString(),
osim.createVectorLinspace(N, 0.05, 0.05))
solver.setGuess(guess)
solver.set_parameters_require_initsystem(False)
solution = moco.solve()
# moco.visualize(solution)
solution.write(self.predict_assisted_solution_file % root_dir)
# Set to a percentage of the final guess time to end
timeLB = 0.9 * osim.Storage(taskName + '_StartingGuess.sto').getLastTime()
timeUB = 1.1 * osim.Storage(taskName + '_StartingGuess.sto').getLastTime()
problem.setTimeBounds(osim.MocoInitialBounds(0.0),
osim.MocoFinalBounds(timeLB, timeUB))
# problem.setTimeBounds(0,[])
#Define marker end point goals
osimHelper.addMarkerEndPoints(taskName, problem, simModel)
#Define kinematic task bounds
osimHelper.addTaskBounds(taskName, problem, simModel, taskBoundsElv,
taskBoundsRot, taskBoundsAng)
#Add a control cost to the problem
problem.addGoal(osim.MocoControlGoal('effort', 1))
#Add a final time goal to the problem
problem.addGoal(osim.MocoFinalTimeGoal('time', 1))
#Configure the solver
solver = study.initCasADiSolver()
solver.set_num_mesh_intervals(meshInterval)
solver.set_verbosity(2)
solver.set_optim_solver('ipopt')
solver.set_optim_convergence_tolerance(1e-4)
solver.set_optim_constraint_tolerance(1e-4)
#Set the guess to an existing solution for this task from existing work
#Note that this will be slightly off given the previous study included forces
#that approximated ligaments and joint capsule passive resistance, but should
# Time bounds
problem.setTimeBounds(0, 1)
# Position bounds: the model should start in a squat and finish
# standing up.
problem.setStateInfo('/jointset/hip_r/hip_flexion_r/value', [-2, 0.5], -2, 0)
problem.setStateInfo('/jointset/knee_r/knee_angle_r/value', [-2, 0], -2, 0)
problem.setStateInfo('/jointset/ankle_r/ankle_angle_r/value', [-0.5, 0.7],
-0.5, 0)
# Velocity bounds: all model coordinates should start and end at rest.
problem.setStateInfoPattern('/jointset/.*/speed', [], 0, 0)
# Part 1d: Add a MocoControlCost to the problem.
problem.addGoal(osim.MocoControlGoal('myeffort'))
# Part 1e: Configure the solver.
solver = study.initCasADiSolver()
solver.set_num_mesh_intervals(25)
solver.set_optim_convergence_tolerance(1e-4)
solver.set_optim_constraint_tolerance(1e-4)
if not os.path.isfile('predictSolution.sto'):
# Part 1f: Solve! Write the solution to file, and visualize.
predictSolution = study.solve()
predictSolution.write('predictSolution.sto')
study.visualize(predictSolution)
## Part 2: Torque-driven Tracking Problem
# Part 2a: Construct a tracking reference TimeSeriesTable using filtered
def run_tracking_problem(self, root_dir, config):
modelProcessor = self.create_model_processor(root_dir,
for_inverse=False,
config=config)
model = modelProcessor.process()
model.initSystem()
# Count the number of Force objects in the model. We'll use this to
# normalize the control effort cost.
numForces = 0
for actu in model.getComponentsList():
if (actu.getConcreteClassName().endswith('Muscle') or
actu.getConcreteClassName().endswith('Actuator')):
numForces += 1
# Construct the base tracking problem
# -----------------------------------
track = osim.MocoTrack()
track.setName('tracking_walking')
track.setModel(modelProcessor)
if self.coordinate_tracking:
tableProcessor = osim.TableProcessor(os.path.join(root_dir,
'resources/Rajagopal2016/coordinates.mot'))
tableProcessor.append(osim.TabOpLowPassFilter(6))
tableProcessor.append(osim.TabOpUseAbsoluteStateNames())
track.setStatesReference(tableProcessor)
track.set_states_global_tracking_weight(
config.tracking_weight / (2 * model.getNumCoordinates()))
# Don't track some pelvis coordinates to avoid poor walking motion
# solutions.
stateWeights = osim.MocoWeightSet()
weightList = list()
weightList.append(('/jointset/ground_pelvis/pelvis_ty/value', 0))
weightList.append(('/jointset/ground_pelvis/pelvis_tz/value', 0))
weightList.append(('/jointset/ground_pelvis/pelvis_list/value', 0))
weightList.append(('/jointset/ground_pelvis/pelvis_tilt/value', 0))
weightList.append(('/jointset/ground_pelvis/pelvis_rotation/value', 0))
weightList.append(('/jointset/hip_r/hip_rotation_r/value', 0))
# weightList.append(('/jointset/hip_r/hip_adduction_r/value', 0))
weightList.append(('/jointset/hip_l/hip_rotation_l/value', 0))
# weightList.append(('/jointset/hip_l/hip_adduction_l/value', 0))
# weightList.append(('/jointset/ankle_r/ankle_angle_r/value', 10))
# weightList.append(('/jointset/ankle_l/ankle_angle_l/value', 10))
for weight in weightList:
stateWeights.cloneAndAppend(osim.MocoWeight(weight[0], weight[1]))
track.set_states_weight_set(stateWeights)
track.set_apply_tracked_states_to_guess(True)
# track.set_scale_state_weights_with_range(True);
else:
track.setMarkersReferenceFromTRC(os.path.join(root_dir,
'resources/Rajagopal2016/markers.trc'))
track.set_markers_global_tracking_weight(
config.tracking_weight / (2 * model.getNumMarkers()))
iktool = osim.InverseKinematicsTool(os.path.join(root_dir,
'resources/Rajagopal2016/ik_setup_walk.xml'))
iktasks = iktool.getIKTaskSet()
markerWeights = osim.MocoWeightSet()
for marker in model.getComponentsList():
if not type(marker) is osim.Marker: continue
for i in np.arange(iktasks.getSize()):
iktask = iktasks.get(int(i))
if iktask.getName() == marker.getName():
weight = osim.MocoWeight(iktask.getName(),
iktask.getWeight())
markerWeights.cloneAndAppend(weight)
track.set_markers_weight_set(markerWeights)
track.set_allow_unused_references(True)
track.set_track_reference_position_derivatives(True)
track.set_control_effort_weight(config.effort_weight / numForces)
track.set_initial_time(self.initial_time)
track.set_final_time(self.half_time)
track.set_mesh_interval(self.mesh_interval)
# Customize the base tracking problem
# -----------------------------------
study = track.initialize()
problem = study.updProblem()
problem.setTimeBounds(self.initial_time,
[self.half_time-0.2, self.half_time+0.2])
# Set the initial values for the lumbar and pelvis coordinates that
# produce "normal" walking motions.
problem.setStateInfo('/jointset/back/lumbar_extension/value', [], -0.12)
problem.setStateInfo('/jointset/back/lumbar_bending/value', [], 0)
problem.setStateInfo('/jointset/back/lumbar_rotation/value', [], 0.04)
problem.setStateInfo('/jointset/ground_pelvis/pelvis_tx/value', [], 0.446)
problem.setStateInfo('/jointset/ground_pelvis/pelvis_tilt/value', [], 0)
problem.setStateInfo('/jointset/ground_pelvis/pelvis_list/value', [], 0)
problem.setStateInfo('/jointset/ground_pelvis/pelvis_rotation/value', [], 0)
# Update the control effort goal to a cost of transport type cost.
effort = osim.MocoControlGoal().safeDownCast(
problem.updGoal('control_effort'))
effort.setDivideByDisplacement(True)
# Weight residual and reserve actuators low in the effort cost since
# they are already weak.
if config.effort_weight:
for actu in model.getComponentsList():
actuName = actu.getName()
if actu.getConcreteClassName().endswith('Actuator'):
effort.setWeightForControl(actu.getAbsolutePathString(),
0.001)
for muscle in ['psoas', 'iliacus']:
for side in ['l', 'r']:
effort.setWeightForControl(
'/forceset/%s_%s' % (muscle, side), 0.25)
speedGoal = osim.MocoAverageSpeedGoal('speed')
speedGoal.set_desired_average_speed(1.235)
problem.addGoal(speedGoal)
# MocoFrameDistanceConstraint
# ---------------------------
if self.coordinate_tracking:
distanceConstraint = osim.MocoFrameDistanceConstraint()
distanceConstraint.setName('distance_constraint')
# Step width is 0.13 * leg_length
# distance = 0.10 # TODO Should be closer to 0.11.
# Donelan JM, Kram R, Kuo AD. Mechanical and metabolic determinants
# of the preferred step width in human walking.
# Proc Biol Sci. 2001;268(1480):1985–1992.
# doi:10.1098/rspb.2001.1761
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1088839/
distanceConstraint.addFramePair(
osim.MocoFrameDistanceConstraintPair(
'/bodyset/calcn_l', '/bodyset/calcn_r', 0.09, np.inf))
distanceConstraint.addFramePair(
osim.MocoFrameDistanceConstraintPair(
'/bodyset/toes_l', '/bodyset/toes_r', 0.06, np.inf))
# distanceConstraint.addFramePair(
# osim.MocoFrameDistanceConstraintPair(
# '/bodyset/calcn_l', '/bodyset/toes_r', distance, np.inf))
# distanceConstraint.addFramePair(
# osim.MocoFrameDistanceConstraintPair(
# '/bodyset/toes_l', '/bodyset/calcn_r', distance, np.inf))
distanceConstraint.setProjection("vector")
distanceConstraint.setProjectionVector(osim.Vec3(0, 0, 1))
problem.addPathConstraint(distanceConstraint)
# Symmetry constraints
# --------------------
statesRef = osim.TimeSeriesTable('tracking_walking_tracked_states.sto')
initIndex = statesRef.getNearestRowIndexForTime(self.initial_time)
symmetry = osim.MocoPeriodicityGoal('symmetry')
# Symmetric coordinate values (except for pelvis_tx) and speeds.
for coord in model.getComponentsList():
if not type(coord) is osim.Coordinate: continue
coordName = coord.getName()
coordValue = coord.getStateVariableNames().get(0)
coordSpeed = coord.getStateVariableNames().get(1)
if coordName.endswith('_r'):
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordValue, coordValue.replace('_r/', '_l/')))
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordSpeed, coordSpeed.replace('_r/', '_l/')))
elif coordName.endswith('_l'):
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordValue, coordValue.replace('_l/', '_r/')))
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordSpeed, coordSpeed.replace('_l/', '_r/')))
elif (coordName.endswith('_bending') or
coordName.endswith('_rotation') or
coordName.endswith('_tz') or
coordName.endswith('_list')):
# This does not include hip rotation,
# because that ends with _l or _r.
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordValue))
symmetry.addNegatedStatePair(osim.MocoPeriodicityGoalPair(
coordSpeed))
elif not coordName.endswith('_tx'):
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordValue))
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
coordSpeed))
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
'/jointset/ground_pelvis/pelvis_tx/speed'))
# Symmetric activations.
for actu in model.getComponentsList():
if (not actu.getConcreteClassName().endswith('Muscle') and
not actu.getConcreteClassName().endswith('Actuator')): continue
if actu.getName().endswith('_r'):
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
actu.getStateVariableNames().get(0),
actu.getStateVariableNames().get(0).replace('_r/', '_l/')))
elif actu.getName().endswith('_l'):
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
actu.getStateVariableNames().get(0),
actu.getStateVariableNames().get(0).replace('_l/', '_r/')))
elif (actu.getName().endswith('_bending') or
actu.getName().endswith('_rotation') or
actu.getName().endswith('_tz') or
actu.getName().endswith('_list')):
symmetry.addNegatedStatePair(osim.MocoPeriodicityGoalPair(
actu.getStateVariableNames().get(0),
actu.getStateVariableNames().get(0)))
else:
symmetry.addStatePair(osim.MocoPeriodicityGoalPair(
actu.getStateVariableNames().get(0),
actu.getStateVariableNames().get(0)))
problem.addGoal(symmetry)
# Contact tracking
# ----------------
forceNamesRightFoot = ['forceset/contactSphereHeel_r',
'forceset/contactLateralRearfoot_r',
'forceset/contactLateralMidfoot_r',
'forceset/contactLateralToe_r',
'forceset/contactMedialToe_r',
'forceset/contactMedialMidfoot_r']
forceNamesLeftFoot = ['forceset/contactSphereHeel_l',
'forceset/contactLateralRearfoot_l',
'forceset/contactLateralMidfoot_l',
'forceset/contactLateralToe_l',
'forceset/contactMedialToe_l',
'forceset/contactMedialMidfoot_l']
if self.contact_tracking:
contactTracking = osim.MocoContactTrackingGoal('contact', 0.001)
contactTracking.setExternalLoadsFile(
'resources/Rajagopal2016/grf_walk.xml')
contactTracking.addContactGroup(forceNamesRightFoot, 'Right_GRF')
contactTracking.addContactGroup(forceNamesLeftFoot, 'Left_GRF')
contactTracking.setProjection("plane")
contactTracking.setProjectionVector(osim.Vec3(0, 0, 1))
problem.addGoal(contactTracking)
# Configure the solver
# --------------------
solver = osim.MocoCasADiSolver.safeDownCast(study.updSolver())
solver.resetProblem(problem)
solver.set_optim_constraint_tolerance(1e-3)
solver.set_optim_convergence_tolerance(1e-3)
solver.set_multibody_dynamics_mode('implicit')
solver.set_minimize_implicit_multibody_accelerations(True)
solver.set_implicit_multibody_accelerations_weight(
1e-6 / model.getNumCoordinates())
solver.set_implicit_multibody_acceleration_bounds(
osim.MocoBounds(-200, 200))
# Set the guess
# -------------
# Create a guess compatible with this problem.
guess = solver.createGuess()
# Load the inverse problem solution and set its states and controls
# trajectories to the guess.
inverseSolution = osim.MocoTrajectory(
os.path.join(root_dir, self.inverse_solution_relpath))
inverseStatesTable = inverseSolution.exportToStatesTable()
guess.insertStatesTrajectory(inverseStatesTable, True)
# Changing this initial guess has a large negative effect on the
# solution! Lots of knee flexion.
# guess.setState('/jointset/ground_pelvis/pelvis_ty/value',
# osim.Vector(guess.getNumTimes(), 1.01))
inverseControlsTable = inverseSolution.exportToControlsTable()
guess.insertControlsTrajectory(inverseControlsTable, True)
solver.setGuess(guess)
# Solve and print solution.
# -------------------------
solution = study.solve()
solution.write(self.get_solution_path(root_dir, config))
# Create a full gait cycle trajectory from the periodic solution.
fullTraj = osim.createPeriodicTrajectory(solution)
fullTraj.write(self.get_solution_path_fullcycle(root_dir, config))
# Compute ground reaction forces generated by contact sphere from the
# full gait cycle trajectory.
externalLoads = osim.createExternalLoadsTableForGait(
model, fullTraj, forceNamesRightFoot, forceNamesLeftFoot)
osim.writeTableToFile(externalLoads,
self.get_solution_path_grfs(root_dir, config))