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 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)
ignore_tendon_compliance = False
model = createHangingMuscleModel(ignore_activation_dynamics,
ignore_tendon_compliance)
model.printToXML("hanging_muscle.osim")
study = osim.MocoStudy()
problem = study.updProblem()
problem.setModelAsCopy(model)
problem.setTimeBounds(0, [0.05, 1.0])
problem.setStateInfo("/joint/height/value", [0.14, 0.16], 0.15, 0.14)
problem.setStateInfo("/joint/height/speed", [-1, 1], 0, 0)
problem.setControlInfo("/forceset/muscle", [0.01, 1])
# Initial state constraints/costs.
if not ignore_activation_dynamics:
initial_activation = osim.MocoInitialActivationGoal()
problem.addGoal(initial_activation)
initial_activation.setName("initial_activation")
if not ignore_tendon_compliance:
initial_equilibrium = osim.MocoInitialVelocityEquilibriumDGFGoal()
problem.addGoal(initial_equilibrium)
initial_equilibrium.setName("initial_velocity_equilibrium")
# The problem converges in fewer iterations when this goal is in cost mode.
initial_equilibrium.setMode("cost")
initial_equilibrium.setWeight(0.001)
problem.addGoal(osim.MocoFinalTimeGoal())
solver = study.initCasADiSolver()
solver.set_num_mesh_intervals(25)
solver.set_multibody_dynamics_mode("implicit")