API  4.3
For MATLAB, Python, Java, and C++ users
Moco User Guide

Table of Contents


Getting started

To learn how to download, install, and run your first Moco example, see the following page:

MocoTools

Moco provides easy-to-use tools for solving for muscle activity that can achieve an observed motion. See the following pages:

These tools derive from the MocoTool class.

Solving custom optimal control problems

If the MocoTools do not satisfy your needs, you can create and solve your own custom optimal control problem using the MocoStudy class.

MocoStudy: custom optimal control problems

Choosing a Model

Moco supports most OpenSim models, including those with kinematic constraints, auxiliary (muscle) dynamics, and passive force components.

Currently, Moco does not support models with Controllers. Moco must serve as the sole controller of the model. This prevents, for example, using reflex controllers to model spasticity. We hope to add support for Model Controllers in the future.

Moco does not support models with SimTK::Measures, such as Probes. Some Measures rely on time-stepping forward integration, which does not occur with Moco. This prevents using, for example, the metabolics probes with Moco.

Some model components are supported through Moco but may hinder the solvers from converging quickly. Any components with discontinuous functions can cause issues for the derivative-based optimization algorithms used by Moco. We suggest replacing your model's muscles with DeGrooteFregly2016Muscles and replacing your contact forces with SmoothSphereHalfSpaceForces. These components are designed for use with direct collocation.

Defining a custom goal or cost

Moco allows you to compose your cost from multiple existing cost terms. This gives you flexibility and means that you usually do not need to write your cost yourself, even if it has many terms. Moco's existing costs are usually well-tested and work well with Moco's solvers, so we encourage you to use them if possible. In the case that you want to create your own custom cost term, you can derive from MocoGoal in C++. See exampleSlidingMassAdvanced.cpp and exampleMocoCustomEffortGoal.cpp.

If you do not want to write C++ code, you can prototype your cost in MATLAB and we can help convert it to C++. See the Matlab example examplePrototypeCustomGoal.m for more information.

In the future, Moco could allow you to define custom cost terms in Python. If this is of interest to you, please let the Moco developers know (on GitHub).

Sealed solutions or trajectories

Moco uses the MocoTrajectory and MocoSolution classes to hold initial guesses and solutions to optimal control problems. Solving a problem (MocoStudy::solve()) returns a MocoSolution. If the solver failed to converge, the solution will be sealed (MocoTrajectory::isSealed()), which means that you cannot do anything with the it (read, edit, or write) until you call MocoTrajectory::unseal(). The sealing forces you to acknowledge that the solver failed.

Utilities

OpenSim and Moco contain utilities for creating models, modifying models, working with data, and postprocessing results.

  1. ModelFactory: Create standard models and modify existing models.
  2. ModelProcessor: Create a workflow of operations to perform on a model.
  3. TableProcessor: Create a workflow of operations to perform on a table.
  4. Moco Utilities, including visualizing and analyzing a MocoTrajectory.
  5. plot_trajectory.py: Plot a MocoTrajectory with the Python Matplotlib plotting library.
  6. report.py: Generate a detailed PDF report about a MocoTrajectory and any reference data (part of the opensim Python package).

Frequently Asked Questions

See Moco frequently asked questions.

Cheat sheet

This short PDF shows common Moco commands, and makes for a nice desk-side companion.