exampleMocoTrack.py

This is an example using the MocoTrack tool with a complex model to track walking.

# -------------------------------------------------------------------------- #
# OpenSim Moco: exampleMocoTrack.py                                          #
# -------------------------------------------------------------------------- #
# Copyright (c) 2023 Stanford University and the Authors                     #
#                                                                            #
# Author(s): Nicholas Bianco                                                 #
#                                                                            #
# Licensed under the Apache License, Version 2.0 (the "License") you may     #
# not use this file except in compliance with the License. You may obtain a  #
# copy of the License at http://www.apache.org/licenses/LICENSE-2.0          #
#                                                                            #
# Unless required by applicable law or agreed to in writing, software        #
# distributed under the License is distributed on an "AS IS" BASIS,          #
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.   #
# See the License for the specific language governing permissions and        #
# limitations under the License.                                             #
# -------------------------------------------------------------------------- #

# This example features two different tracking problems solved using the
# MocoTrack tool. 
#  - The first problem demonstrates the basic usage of the tool interface
#    to solve a torque-driven marker tracking problem. 
#  - The second problem shows how to customize a muscle-driven state tracking 
#    problem using more advanced features of the tool interface.
# 
# See the README.txt next to this file for more information.

import os
import opensim as osim

def torqueDrivenMarkerTracking():

    # Create and name an instance of the MocoTrack tool.
    track = osim.MocoTrack()
    track.setName("torque_driven_marker_tracking")

    # Construct a ModelProcessor and add it to the tool. ModelProcessors
    # accept a base model and allow you to easily modify the model by appending
    # ModelOperators. Operations are performed in the order that they are
    # appended to the model.
    # Create the base Model by passing in the model file.
    modelProcessor = osim.ModelProcessor("subject_walk_scaled.osim")
    # Add ground reaction external loads in lieu of a ground-contact model.
    modelProcessor.append(osim.ModOpAddExternalLoads("grf_walk.xml"))
    # Remove all the muscles in the model's ForceSet.
    modelProcessor.append(osim.ModOpRemoveMuscles())
    # Add CoordinateActuators to the model degrees-of-freedom. This ignores the 
    # pelvis coordinates which already have residual CoordinateActuators.
    modelProcessor.append(osim.ModOpAddReserves(250.0, 1.0))
    track.setModel(modelProcessor)

    # Use this convenience function to set the MocoTrack markers reference
    # directly from a TRC file. By default, the markers data is filtered at
    # 6 Hz and if in millimeters, converted to meters.
    track.setMarkersReferenceFromTRC("marker_trajectories.trc")

    # There is marker data in the 'marker_trajectories.trc' associated with
    # model markers that no longer exists (i.e. markers on the arms). Set this
    # flag to avoid an exception from being thrown.
    track.set_allow_unused_references(True)

    # Increase the global marker tracking weight, which is the weight
    # associated with the internal MocoMarkerTrackingCost term.
    track.set_markers_global_tracking_weight(10)

    # Increase the tracking weights for individual markers in the data set 
    # placed on bony landmarks compared to markers located on soft tissue.
    markerWeights = osim.MocoWeightSet()
    markerWeights.cloneAndAppend(osim.MocoWeight("R.ASIS", 20))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.ASIS", 20))
    markerWeights.cloneAndAppend(osim.MocoWeight("R.PSIS", 20))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.PSIS", 20))
    markerWeights.cloneAndAppend(osim.MocoWeight("R.Knee", 10))
    markerWeights.cloneAndAppend(osim.MocoWeight("R.Ankle", 10))
    markerWeights.cloneAndAppend(osim.MocoWeight("R.Heel", 10))
    markerWeights.cloneAndAppend(osim.MocoWeight("R.MT5", 5))
    markerWeights.cloneAndAppend(osim.MocoWeight("R.Toe", 2))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.Knee", 10))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.Ankle", 10))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.Heel", 10))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.MT5", 5))
    markerWeights.cloneAndAppend(osim.MocoWeight("L.Toe", 2))
    track.set_markers_weight_set(markerWeights)

    # Initial time, final time, and mesh interval. The number of mesh points
    # used to discretize the problem is computed internally using these values.
    track.set_initial_time(0.48)
    track.set_final_time(1.61)
    track.set_mesh_interval(0.02)

    # Solve! Use track.solve() to skip visualizing.
    solution = track.solveAndVisualize()

def muscleDrivenStateTracking():

    # Create and name an instance of the MocoTrack tool.
    track = osim.MocoTrack()
    track.setName("muscle_driven_state_tracking")

    # Construct a ModelProcessor and set it on the tool. The default
    # muscles in the model are replaced with optimization-friendly
    # DeGrooteFregly2016Muscles, and adjustments are made to the default muscle
    # parameters.
    modelProcessor = osim.ModelProcessor("subject_walk_scaled.osim")
    modelProcessor.append(osim.ModOpAddExternalLoads("grf_walk.xml"))
    modelProcessor.append(osim.ModOpIgnoreTendonCompliance())
    modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())
    # Only valid for DeGrooteFregly2016Muscles.
    modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())
    # Only valid for DeGrooteFregly2016Muscles.
    modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))
    # Use a function-based representation for the muscle paths. This is
    # recommended to speed up convergence, but if you would like to use
    # the original GeometryPath muscle wrapping instead, simply comment out
    # this line. To learn how to create a set of function-based paths for
    # your model, see the example 'examplePolynomialPathFitter.py'.
    modelProcessor.append(osim.ModOpReplacePathsWithFunctionBasedPaths(
            "subject_walk_scaled_FunctionBasedPathSet.xml"))
    track.setModel(modelProcessor)

    # Construct a TableProcessor of the coordinate data and pass it to the 
    # tracking tool. TableProcessors can be used in the same way as
    # ModelProcessors by appending TableOperators to modify the base table.
    # A TableProcessor with no operators, as we have here, simply returns the
    # base table.
    track.setStatesReference(osim.TableProcessor("coordinates.sto"))

    # This setting allows extra data columns contained in the states
    # reference that don't correspond to model coordinates.
    track.set_allow_unused_references(True)

    # Since there is only coordinate position data in the states references,
    # this setting is enabled to fill in the missing coordinate speed data using
    # the derivative of splined position data.
    track.set_track_reference_position_derivatives(True)

    # Initial time, final time, and mesh interval.
    track.set_initial_time(0.48)
    track.set_final_time(1.61)
    track.set_mesh_interval(0.02)

    # Instead of calling solve(), call initialize() to receive a pre-configured
    # MocoStudy object based on the settings above. Use this to customize the
    # problem beyond the MocoTrack interface.
    study = track.initialize()

    # Get a reference to the MocoControlCost that is added to every MocoTrack
    # problem by default.
    problem = study.updProblem()
    effort = osim.MocoControlGoal.safeDownCast(problem.updGoal("control_effort"))
    effort.setWeight(0.1)

    # Put a large weight on the pelvis CoordinateActuators, which act as the
    # residual, or 'hand-of-god', forces which we would like to keep as small
    # as possible.
    model = modelProcessor.process()
    model.initSystem()
    forceSet = model.getForceSet()
    for i in range(forceSet.getSize()):
        forcePath = forceSet.get(i).getAbsolutePathString()
        if 'pelvis' in str(forcePath):
            effort.setWeightForControl(forcePath, 10)

    # Constrain the muscle activations at the initial time point to equal
    # the initial muscle excitation value.
    problem.addGoal(osim.MocoInitialActivationGoal('initial_activation'))

    # Update the solver tolerances.
    solver = osim.MocoCasADiSolver.safeDownCast(study.updSolver())
    solver.set_optim_convergence_tolerance(1e-3)
    solver.set_optim_constraint_tolerance(1e-4)
    
    # Solve and visualize.
    solution = study.solve()
    study.visualize(solution)


# Solve the torque-driven marker tracking problem.
torqueDrivenMarkerTracking()

# Solve the muscle-driven state tracking problem.
muscleDrivenStateTracking()