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What is SCONE?

SCONE is open source software that aids in performing predictive simulations of biological motion.

Predictive simulations do not rely on recorded motion data to estimate muscle force or joint torque. Instead, they compute motion trajectories that perform a given task optimally, according to high-level objectives such as stability, energy efficiency and pain avoidance.

Predictive simulations enable powerful new applications for musculoskeletal models, such as predicting the outcome of treatment, and optimizing the efficiency and efficacy of assistive devices. More fundamentally, it enables researchers to pose true what-if? questions, allowing them to investigate the effects of individual model and control parameters on the motion as a whole.

How does SCONE work?

In SCONE, everything needed to perform a predictive simulation is bundled in what is called a scenario. Each SCONE scenario consists of the following components (click to expand):


The central part of a predictive simulation is the (musculoskeletal) model of the entity you wish to simulate. SCONE is designed to work with OpenSim for modeling and simulation. Any OpenSim model is supported – both 2D and 3D – as long as the model contains controllable actuators, such as muscle-tendon units or torque-based actuators. For simulating contact reaction forces, a model also requires collision shapes and contact models. Finally, models typically contain limit forces to simulate the forces generated by tendons and bony structures when a joint reaches a certain limit.

Always try to keep your model as simple as possible! Each actuator you add to the model requires additional control parameters that need to be optimized. Each element you add to a model makes the simulation run slower.


Controllers compute the input values of the model actuators. They exist in two flavors:

  • Feed-forward (or open-loop) controllers, which generate fixed patterns based on a parameterized function
  • Feedback (or closed-loop) controllers, which generate actuator inputs based on sensor information

SCONE uses a modular system in which controllers can combined to perform complex behaviors.


The goal of a predictive simulation is to find the motion pattern for which a specific task is performed optimally. Such a task is represented by an objective function, which returns a number that indicates how well a task is performed. In SCONE, objective functions are defined as a weighted combination of so-called Measures; examples of which are walking speed, energy expenditure, center-of-mass position, etc.


The goal of an Optimizer is to find the parameters for which an objective function is minimized (or maximized, depending on the type of objective). SCONE uses a shooting-based approach to optimization, in which a simulation is performed multiple times, and parameters are adjusted according the result of the objective function

To learn more about these components, be sure check out the tutorials.

Who is SCONE for?

SCONE is designed for biomechanics, robotics and neuromechanics researchers or enthusiasts who wish to use predictive simulations in their research. For more information, see the Frequently Asked Questions.