simtk.org

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Warning: intermediate_callback is disfunctional in your installation. You will only be able to use stats(). See https://github.com/casadi/casadi/wiki/enableIpoptCallback to enable it.
CasADi - 2019-12-02 12:36:39 WARNING("nlp:nlp_jac_g failed: NaN detected for output jac_g_x, at nonzero index 94 (row 94, col 0).") [C:\projects\opensim-moco\dependencies\casadi\casadi\core\oracle_function.cpp:265]
CasADi - 2019-12-02 12:36:47 WARNING("nlp:nlp_jac_g failed: NaN detected for output jac_g_x, at nonzero index 94 (row 94, col 0).") [C:\projects\opensim-moco\dependencies\casadi\casadi\core\oracle_function.cpp:265]
MocoCasADiSolver did NOT succeed:
  Invalid_Number_Detected

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...
Path constraints: none
-------------------------------------------------------------------------------
Elapsed real time: 25 second(s).
12/02/19 13:53:12
MocoCasADiSolver did NOT succeed:
  Invalid_Number_Detected
===============================================================================
Error using exampleMocoTrack>muscleDrivenStateTracking (line 194)
Java exception occurred:
java.lang.RuntimeException: This iterate is sealed, to force you to acknowledge the solver failed; call unseal() to gain access.
	Thrown at MocoTrajectory.cpp:1302 in ensureUnsealed().

	at org.opensim.modeling.opensimMocoJNI.MocoStudy_visualize(Native Method)

	at org.opensim.modeling.MocoStudy.visualize(MocoStudy.java:129)


Error in exampleMocoTrack (line 54)
muscleDrivenStateTracking();

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 modelProcessor.append(ModOpIgnoreTendonCompliance()); 

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% -------------------------------------------------------------------------- %
% OpenSim Moco: exampleMocoTrack.m                                           %
% -------------------------------------------------------------------------- %
% Copyright (c) 2019 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.
% 
% Data and model source: https://simtk.org/projects/full_body
% 
% Model
% -----
% The model described in the file 'subject_walk_armless.osim' included in this
% file is a modified version of the Rajagopoal et al. 2016 musculoskeletal 
% model. The lumbar, subtalar, and mtp coordinates have been replaced with
% WeldJoints and residual actuators have been added to the pelvis (1 N-m for
% rotational coordinates and 10 N for translational coordinates). Finally, the
% arms and all associated components have been removed for simplicity.
% 
% Data
% ----
% The coordinate and marker data included in the 'coordinates.sto' and 
% 'marker_trajectories.trc' files also come from the Rajagopal et al. 2016
% model distribution. The coordinates were computed using inverse kinematics
% and modified via the Residual Reduction Algorithm (RRA). 

function exampleMocoTrack()

% Solve the torque-driven marker tracking problem.
% This problem takes a few minutes to solve.
torqueDrivenMarkerTracking();

% Solve the muscle-driven state tracking problem.
% This problem could take an hour or more to solve, depending on the number of
% processor cores available for parallelization. With 12 cores, it takes around
% 25 minutes.
muscleDrivenStateTracking();

end

function torqueDrivenMarkerTracking()

import org.opensim.modeling.*;

% Create and name an instance of the MocoTrack tool.
track = 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 = ModelProcessor("subject_walk_armless.osim");
% Add ground reaction external loads in lieu of a ground-contact model.
modelProcessor.append(ModOpAddExternalLoads("grf_walk.xml"));
% Remove all the muscles in the model's ForceSet.
modelProcessor.append(ModOpRemoveMuscles());
% Add CoordinateActuators to the model degrees-of-freedom. This
% ignores the pelvis coordinates which already have residual 
% CoordinateActuators.
modelProcessor.append(ModOpAddReserves(250));
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 MocoMarkerTrackingGoal 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 = MocoWeightSet();
markerWeights.cloneAndAppend(MocoWeight("R.ASIS", 20));
markerWeights.cloneAndAppend(MocoWeight("L.ASIS", 20));
markerWeights.cloneAndAppend(MocoWeight("R.PSIS", 20));
markerWeights.cloneAndAppend(MocoWeight("L.PSIS", 20));
markerWeights.cloneAndAppend(MocoWeight("R.Knee", 10));
markerWeights.cloneAndAppend(MocoWeight("R.Ankle", 10));
markerWeights.cloneAndAppend(MocoWeight("R.Heel", 10));
markerWeights.cloneAndAppend(MocoWeight("R.MT5", 5));
markerWeights.cloneAndAppend(MocoWeight("R.Toe", 2));
markerWeights.cloneAndAppend(MocoWeight("L.Knee", 10));
markerWeights.cloneAndAppend(MocoWeight("L.Ankle", 10));
markerWeights.cloneAndAppend(MocoWeight("L.Heel", 10));
markerWeights.cloneAndAppend(MocoWeight("L.MT5", 5));
markerWeights.cloneAndAppend(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(0.81);
track.set_final_time(1.65);
track.set_mesh_interval(0.05);

% Solve! The boolean argument indicates to visualize the solution.
solution = track.solve(true);

end

function muscleDrivenStateTracking()

import org.opensim.modeling.*;

% Create and name an instance of the MocoTrack tool. 
track = 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 = ModelProcessor("subject_walk_armless.osim");
modelProcessor.append(ModOpAddExternalLoads("grf_walk.xml"));
modelProcessor.append(ModOpReplaceMusclesWithDeGrooteFregly2016());
% Only valid for DeGrooteFregly2016Muscles.
modelProcessor.append(ModOpIgnorePassiveFiberForcesDGF());
% Only valid for DeGrooteFregly2016Muscles.
modelProcessor.append(ModOpScaleActiveFiberForceCurveWidthDGF(1.5));
track.setModel(modelProcessor);
modelProcessor.append(ModOpIgnoreTendonCompliance());

% 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(TableProcessor("coordinates.sto"));
track.set_states_global_tracking_weight(10);


% 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 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(0.81);
track.set_final_time(1.65);
track.set_mesh_interval(0.08);

% 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 MocoControlGoal that is added to every MocoTrack
% problem by default.
problem = study.updProblem();
effort = MocoControlGoal.safeDownCast(problem.updGoal("control_effort"));

% 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 = 0:forceSet.getSize()-1
   forcePath = forceSet.get(i).getAbsolutePathString();
   if contains(string(forcePath), 'pelvis')
       effort.setWeightForControl(forcePath, 10);
   end
end

% Solve and visualize.
solution = study.solve();
study.visualize(solution);

end