Bioptim is an optimal control program (OCP) framework for biomechanics. It is based on the efficient biorbd biomechanics library and benefits from the powerful algorithmic diff provided by CasADi. It interfaces the robust Ipopt and the fast Acados solvers to suit all your needs for solving OCP in biomechanics.

Type	Status
License
Continuous integration
Code coverage
DOI

The current status of bioptim on conda-forge is

Name	Downloads	Version	Platforms	MyBinder

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Testing bioptim

How to install

• From anaconda
• From the sources
• Installation complete

A first practical example

• The import
• Building the ocp
• Solving the ocp
• Show the results
• The full example files

A more in depth look at the bioptim API

• The OCP
  • OptimalControlProgram
  • NonLinearProgram
• The dynamics
  • Dynamics
  • DynamicsList
  • DynamicsFcn
• The bounds
  • Bounds
  • BoundsList
  • QAndQDotBounds
• The initial conditions
  • InitialGuess
  • InitialGuessList
• The constraints
  • Constraint
  • ConstraintList
  • ConstraintFcn
• The objective functions
  • Objective
  • ObjectiveList
  • ObjectiveFcn
• The parameters
  • ParameterList
• The phase transitions
  • PhaseTransitionList
  • PhaseTransitionFcn
• The results
  • Data manipulation
  • Data visualization
• The extra stuff and the Enum
  • The mappings
  • Node
  • OdeSolver
  • Solver
  • ControlType
  • PlotType
  • InterpolationType
  • Shooting
  • CostType

Examples

• Getting started
• Muscle driven OCP
• Muscle driven with contact
• Optimal time OCP
• Symmetrical torque driven OCP
• Torque driven OCP
• Track
• Moving estimation horizon
• Acados

Citing

The preferred way to install for the lay user is using anaconda. Another way, more designed for the core programmers is from the sources. While it is theoretically possible to use bioptim from Windows, it is highly discouraged since it will require to manually compile all the dependencies. A great alternative for the Windows users is Ubuntu on Windows supporting Linux.

The easiest way to install bioptim is to download the binaries from Anaconda repositories. The project is hosted on the conda-forge channel (https://anaconda.org/conda-forge/bioptim).

After having installed properly an anaconda client [my suggestion would be Miniconda (https://conda.io/miniconda.html)] and loaded the desired environment to install bioptim in, just type the following command:

conda install -c conda-forge bioptim

This will download and install all the dependencies and install bioptim. And that is it! You can already enjoy bioptiming!

Installing from the sources is basically as easy as installing from Anaconda, with the difference that you will be required to download and install the dependencies by hand (see section below).

bioptim relies on several libraries. The most obvious one is the biorbd suite (including indeed biorbd and bioviz), but some extra more are required. Due to the amount of different dependencies, it would be tedious to show how to install them all here. The user is therefore invited to read the relevant documentations.

Here is a list of all direct dependencies (meaning that some dependencies may require other libraries themselves):

• Python
• numpy
• scipy
• packaging
• setuptools
• matplotlib
• pandas
• pyomeca
• CasADi
• rbdl-casadi compiled with the CasADi backend
• tinyxml
• biorbd
• vtk
• PyQt
• bioviz
• graphviz
• Ipopt
• Acados

and optionally:

• The linear solvers from the HSL Mathematical Software Library

All these (except for ̀Acados and the HSL lib) can easily be installed using (assuming the anaconda3 environment is loaded if needed) the pip3 command, or the Anaconda's following command:

conda install casadi rbdl=*=*casadi* biorbd=*=*casadi* bioviz=*=*casadi* python-graphviz -cconda-forge

Since there isn't any Anaconda nor pip3 package of Acados, a convenient installer is provided with bioptim. The installer can be found and run at [ROOT_BIOPTIM]/external/acados_install_linux.sh. However, the installer requires an Anaconda3 environment. If you have an Anaconda3 environment loaded, the installer should find itself where to install. If you want to install elsewhere, you can provide the script with a first argument which is the $CONDA_PREFIX. The second argument that can be passed to the script is the $BLASFEO_TARGET. If you don't know what it is, it is probably better to keep the default. Please note that depending on your computer architecture, Acados may or may not work properly.

Equivalently for MacOSX:

conda install casadi 'rbdl=*=*casadi*' 'biorbd=*=*casadi*' 'bioviz=*=*casadi*' python-graphviz -cconda-forge

Since there isn't any Anaconda nor pip3 package of Acados, a convenient installer is provided with bioptim. The Acados installation script is [ROOT_BIOPTIM]/external/acados_install_mac.sh. However, the installer requires an Anaconda3 environment. If you have an Anaconda3 environment loaded, the installer should find itself where to install. If you want to install elsewhere, you can provide the script with a first argument which is the $CONDA_PREFIX. The second argument that can be passed to the script is the $BLASFEO_TARGET. If you don't know what it is, it is probably better to keep the default. Please note that depending on your computer architecture, Acados may or may not work properly.

Equivalently for Windows:

conda install casadi 'rbdl=*=*casadi*' 'biorbd=*=*casadi*' 'bioviz=*=*casadi*' python-graphviz -cconda-forge

There isn't any Anaconda nor pip3 package of Acados. If one wants to use the Acados solver on Windows, they must compile it by themselves.

HSL is a collection of state-of-the-art packages for large-scale scientific computation. Among its best known packages are those for the solution of sparse linear systems (ma27, ma57, etc.), compatible with ̀Ipopt. HSL packages are available at no cost for academic research and teaching. Once you obtain the HSL dynamic library (precompiled libhsl.so for Linux, to be compiled libhsl.dylib for MacOSX, libhsl.dll for Windows), you just have place it in your Anaconda3 environment into the lib/ folder. You are now able to use all the options of bioptim, including the HSL linear solvers with Ipopt. We recommend that you use ma57 as a default linear solver by calling as such:

ocp.solve(solver_options={"linear_solver": "ma57"})

Once you have downloaded bioptim, navigate to the root folder and (assuming your conda environment is loaded if needed), you can type the following command:

python setup.py install

Assuming everything went well, that is it! You can already enjoy bioptimizing!

The easiest way to learn bioptim is to dive into it. So let's do that and build our first optimal control program together. Please note that this tutorial is designed to recreate the examples/getting_started/pendulum.py file where a pendulum is asked to start in a downward position and to end, balanced, in an upward position while only being able to actively move sideways.

We won't spend time explaining the import, since every one of them will be explained in details later, and that it is pretty straightforward anyway.

import biorbd_casadi as biorbd
from bioptim import (
    OptimalControlProgram,
    DynamicsFcn,
    Dynamics,
    Bounds,
    QAndQDotBounds,
    InitialGuess,
    ObjectiveFcn,
    Objective,
)

First of all, let's load a bioMod file using biorbd:

biorbd_model = biorbd.Model("pendulum.bioMod")

It is convenient since it will provide interesting functions such as the number of degrees of freedom (biorbd_model.nbQ()). Please note that a copy of pendulum.bioMod is available at the end of the Getting started section. In brief, the pendulum consists of two degrees of freedom (sideways movement and rotation) with the center of mass near the head.

The dynamics of the pendulum, as for a lot of biomechanics one as well, is to drive it by the generalized forces. That is forces and moments directly applied to the degrees of freedom as if virtual motors were to power them. In bioptim, this dynamic is called torque driven. In a torque driven dynamics, the states are the positions (also called generalized coordinates, q) and the velocities (also called the generalized velocities, qdot) and the controls are the joint torques (also called generalized forces, tau). Let's define such a dynamics:

dynamics = Dynamics(DynamicsFcn.TORQUE_DRIVEN)

The pendulum is required to start in a downward position (0 rad) and to finish in an upward position (3.14 rad) with no velocity at start and end nodes. To define that, it would be nice to first define boundary constraints on the position (q) and velocities (qdot) that match those in the bioMod file and to apply them at the very beginning, the very end and all the intermediate nodes as well. QAndQDotBounds waits for a biorbd model and returns a structure with the minimal and maximal bounds for all the degrees of freedom and velocities on three columns corresponding to the starting node, the intermediate nodes and the final node, respectively. How convenient!

x_bounds = QAndQDotBounds(biorbd_model)

Then, override the first and last column to be 0, that is the sideways and rotation to be null for both the position and the velocities

x_bounds[:, [0, -1]] = 0

Finally, override once again the final node for the rotation, so it is upside down.

x_bounds[1, -1] = 3.14

At that point, you may want to have a look at the x_bounds.min and x_bounds.max matrices to convince yourself that the initial and final position and velocities are prescribed and that all the intermediate points are free up to a certain minimal and maximal values.

Up to that point, there is nothing preventing the solver to simply use the virtual motor of the rotation to rotate the pendulum upward (like clock hands) to get to the upside down rotation. What makes this example interesting is that we can prevent this by defining minimal and maximal bounds on the control (the maximal forces that these motors have)

u_bounds = Bounds([-100, 0], [100, 0])

Like this, the sideways force ranges from -100 Newton to 100 Newton, but the rotation force ranges from 0 N/m to 0 N/m. Again, u_bound is defined for the first, the intermediate and the final nodes, but this time, we don't want to specify anything particular for the first and final nodes, so we can leave them as is.

Who says optimization, says cost function. Even though, it is possible to define an OCP without objective, it is not so much recommended, and let's face it... much less fun! So the goal (or the cost function) of the pendulum is to perform its task while using the minimum forces as possible. Therefore, an objective function that minimizes the generalized forces is defined:

objective_functions = Objective(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE)

At that point, it is possible to solves the program. Still, helping the solver is usually a good idea, so let's give ̀Ipopt a starting point to investigate. The initial guess that we can provide are those for the states (x_init, here q and qdot) and for the controls (u_init, here tau). So let's define both of them quickly

x_init = InitialGuess([0, 0, 0, 0])
u_init = InitialGuess([0, 0])

Please note that x_init is twice the size of u_init because it contains the two degrees of freedom from the generalized coordinates (q) and the two from the generalized velocities (qdot), while u_init only contains the generalized forces (tau)

We now have everything to create the ocp! For that we have to decide how much time the pendulum has to get up there (phase_time) and how many shooting point are defined for the multishoot (n_shooting). Thereafter, you just have to send everything to the OptimalControlProgram class and let bioptim prepare everything for you. For simplicity's sake, I copy all the piece of code previously visited in the building of the ocp section here:

ocp = OptimalControlProgram(
        biorbd_model,
        dynamics,
        n_shooting=25,
        phase_time=3,
        x_init=x_init,
        u_init=u_init,
        x_bounds=x_bounds,
        u_bounds=u_bounds,
        objective_functions=objective_functions,
    )

It is now time to see Ipopt in action! To solve the ocp, you simply have to call the solve() method of the ocp class

sol = ocp.solve(show_online_optim=True)

If you feel fancy, you can even activate the online optimization graphs! However, for such an easy problem, Ipopt won't leave you the time to appreciate the realtime updates of the graph... For a more complicated problem, you may also wish to visualize the objectives and constraints during the optimization (useful when debugging, because who codes the right thing the first time). You can do it by calling

ocp.add_plot_penalty(CostType.OBJECTIVES)
ocp.add_plot_penalty(CostType.CONSTRAINTS)

or alternatively asks for both at once using

ocp.add_plot_penalty(CostType.ALL)

That's it!

If you want to have a look at the animated data, bioptim has an interface to bioviz which is designed to visualize bioMod files. For that, simply call the animate() method of the solution:

sol.animate()

If you did not fancy the online graphs, but would enjoy them anyway, you can call the method graphs():

sol.graphs()

If you are interested in the results of individual objective functions and constraints, you can print them using the print():

sol.print()

And that is all! You have completed your first optimal control program with bioptim!

If you did not completely follow (or were too lazy to!) you will find in this section the complete files described in the Getting started section. You will find that the file is a bit different from the example/getting_started/pendulum.py, but it is merely differences on the surface.

import biorbd_casadi as biorbd
from bioptim import (
    OptimalControlProgram,
    DynamicsFcn,
    Dynamics,
    Bounds,
    QAndQDotBounds,
    InitialGuess,
    ObjectiveFcn,
    Objective,
)

biorbd_model = biorbd.Model("pendulum.bioMod")
dynamics = Dynamics(DynamicsFcn.TORQUE_DRIVEN)
x_bounds = QAndQDotBounds(biorbd_model)
x_bounds[:, [0, -1]] = 0
x_bounds[1, -1] = 3.14
u_bounds = Bounds([-100, 0], [100, 0])
objective_functions = Objective(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE)
x_init = InitialGuess([0, 0, 0, 0])
u_init = InitialGuess([0, 0])

ocp = OptimalControlProgram(
        biorbd_model,
        dynamics,
        n_shooting=25,
        phase_time=3,
        x_init=x_init,
        u_init=u_init,
        x_bounds=x_bounds,
        u_bounds=u_bounds,
        objective_functions=objective_functions,
    )
    
sol = ocp.solve(show_online_optim=True)
sol.print()
sol.animate()

Here is a simple pendulum that can be interpreted by biorbd. For more information on how to build a bioMod file, one can read the doc of biorbd.

version 4

// Seg1
segment Seg1
    translations	y
    rotations	x
    ranges  -1 5
            -2*pi 2*pi
    mass 1
    inertia
        1 0 0
        0 1 0
        0 0 0.1
    com 0.1 0.1 -1
    mesh 0.0   0.0   0.0
    mesh 0.0  -0.0  -0.9
    mesh 0.0   0.0   0.0
    mesh 0.0   0.2  -0.9
    mesh 0.0   0.0   0.0
    mesh 0.2   0.2  -0.9
    mesh 0.0   0.0   0.0
    mesh 0.2   0.0  -0.9
    mesh 0.0   0.0   0.0
    mesh 0.0  -0.0  -1.1
    mesh 0.0   0.2  -1.1
    mesh 0.0   0.2  -0.9
    mesh 0.0  -0.0  -0.9
    mesh 0.0  -0.0  -1.1
    mesh 0.2  -0.0  -1.1
    mesh 0.2   0.2  -1.1
    mesh 0.0   0.2  -1.1
    mesh 0.2   0.2  -1.1
    mesh 0.2   0.2  -0.9
    mesh 0.0   0.2  -0.9
    mesh 0.2   0.2  -0.9
    mesh 0.2  -0.0  -0.9
    mesh 0.0  -0.0  -0.9
    mesh 0.2  -0.0  -0.9
    mesh 0.2  -0.0  -1.1
endsegment

    // Marker 1
    marker marker_1
        parent Seg1
        position 0 0 0
    endmarker

    // Marker 2
    marker marker_2
        parent Seg1
        position 0.1 0.1 -1
    endmarker

In this section, we are going to have an in depth look at all the classes one can use to interact with the bioptim API. All the classes covered here, can be imported using the command:

from bioptim import ClassName

An optimal control program is an optimization that uses control variables in order to drive some state variables. There are mainly two different types of ocp, which is the direct collocation and the direct multiple shooting. Bioptim is based on the latter. To summarize, it defines a large optimization problem by discretizing the control and the state variables into a predetermined number of intervals, the beginning of which are called the shooting points. By defining strict constraints between the end of an interval and the beginning of the next, it can ensure a proper dynamics of the system, ???->while have good insight to solve the problem using gradient decending algorithms.

This is the main class that holds an ocp. Most of the attributes and methods are for internal use, therefore the API user should not care much about them. Once an OptimalControlProgram is constructed, it is usually ready to be solved.

The full signature of the OptimalControlProgram can be scary at first, but should becomes clear soon. Here it is:

OptimalControlProgram(
    biorbd_model: [str, biorbd.Model, list],
    dynamics: [Dynamics, DynamicsList],
    n_shooting: [int, list],
    phase_time: [float, list],
    x_init: [InitialGuess, InitialGuess]
    u_init: [InitialGuess, InitialGuessList], 
    x_bounds: [Bounds, BoundsList],
    u_bounds: [Bounds, BoundsList],
    objective_functions: [Objective, ObjectiveList],
    constraints: [Constraint, ConstraintList],
    parameters: ParameterList,
    external_forces: list,
    ode_solver: OdeSolver,
    control_type: [ControlType, list],
    all_generalized_mapping: BiMapping,
    q_mapping: BiMapping,
    qdot_mapping: BiMapping,
    tau_mapping: BiMapping,
    plot_mappings: Mapping,
    phase_transitions: PhaseTransitionList,
    n_threads: int,
    use_sx: bool,
)

Of these, only the first 4 are mandatory. biorbd_model is the biorbd model to use. If the model is not loaded, a string can be passed. In the case of a multiphase optimization, one model per phase should be passed in a list. dynamics is the dynamics of the system during each phase (see The dynamics section). n_shooting is the number of shooting point of the direct multiple shooting for each phase. phase_time is the final time of each phase. If the time is free, this is the initial guess. x_init is the initial guess for the states variables (see The initial conditions section) u_init is the initial guess for the controls variables (see The initial conditions section) x_bounds is the minimal and maximal value the states can have (see The bounds section) u_bounds is the minimal and maximal value the controls can have (see The bounds section) objective_functions is the objective function set of the ocp (see The objective functions section) constraints is the constraint set of the ocp (see The constraints section) parameters is the parameter set of the ocp (see The parameters section) external_forces are the external forces acting on the center of mass of the bodies. It is list (one element for each phase) of np.array of shape (6, i, n), where the 6 components are [Mx, My, Mz, Fx, Fy, Fz], for the ith force platform (defined by the externalforceindex) for each node n ode_solver is the ode solver used to solve the dynamic equations control_type is the type of discretization of the controls (usually CONSTANT) (see ControlType section) all_generalized_mapping is used to reduce the number of degrees of freedom by linking them (see The mappings section). This ones applies the same mapping to the generalized coordinates (q), velocities (qdot) and forces (tau). q_mapping the mapping applied to q. qdot_mapping the mapping applied to q_dot. tau_mapping the mapping applied to tau. plot_mappings is to force some plot to be linked together. n_threads is to solve the optimization using multiple thread. This number is the number of thread to use. use_sx is if the CasADi graph should be constructed in SX. SX will tend to solve much faster than MX graphs, however they can necessitate a huge amount of RAM.

Please note that a common ocp will usually define only these parameters:

ocp = OptimalControlProgram(
    biorbd_model: [str, biorbd.Model, list],
    dynamics: [Dynamics, DynamicsList],
    n_shooting: [int, list],
    phase_time: [float, list],
    x_init: [InitialGuess, InitialGuess]
    u_init: [InitialGuess, InitialGuessList], 
    x_bounds: [Bounds, BoundsList],
    u_bounds: [Bounds, BoundsList],
    objective_functions: [Objective, ObjectiveList],
    constraints: [Constraint, ConstraintList],
    n_threads: int,
)

The main methods one will be interested in are:

ocp.update_objectives()
ocp.update_constraints()
ocp.update_parameters()
ocp.update_bounds()
ocp.update_initial_guess()

These allow to modify the ocp after being defined. It is particularly useful when solving the ocp for a first time, and then adjusting some parameters and reoptimizing afterwards.

Moreover, the method

solution = ocp.solve(Solver, solver_options:{})

is called to actually solve the ocp (the solution structure is discussed later). The Solver parameter can be used to select the nonlinear solver to solve the ocp, ̀Ipopt being the default choice. Note that options can be passed to the solver via the solver_options parameter. One can refer to the documentation of their respective chosen solver to know which options exist. The show_online_optim parameter can be set to True so the graphs nicely update during the optimization. It is expected to slow down the optimization a bit though.

Finally, one can save and load previously optimized values by using

ocp.save(solution, file_path)
ocp, solution = OptimalControlProgram.load(file_path)

Please note that this is bioptim version dependent, which means that an optimized solution from a previous version will not probably load on a newer bioptim version. To save the solution in a version independent manner, you may want to manually save the data from the solution.

Finally, the add_plot(name, update_function) method can be used to create new dynamics plots. The name is simply the name of the figure. If one with the same name already exists, then the axes are merged. The update_function is a function handler with signature: update_function(states: np.ndarray, constrols: np.ndarray: parameters: np.ndarray) -> np.ndarray. It is expected to return a np.ndarray((n, 1)), where n is the number of elements to plot. The axes_idx parameter can be added to parse the data in a more exotic manner. For instance, on a three axes figure, if one wanted to plot the first value on the third axes and the second value on the first axes and nothing on the second, the axes_idx=[2, 0] would do the trick. The interested user can have a look at the examples/getting_started/custom_plotting.py example.

The NonLinearProgram is by essence the phase of an ocp. The user is expected not to change anything from this class, but can retrieve useful information from it.

One of the main use of nlp is to get a reference to the biorbd_model for the current phase: nlp.model. Another important value stored in nlp is the shape of the states and controls: nlp.shape, which is a dictionary where the keys are the names of the elements (for instance, q for the generalized coordinates)

It would be tedious, and probably not much useful, to list all the elements of nlp here.
The interested user is invited to have a look at the docstrings for this particular class to get a detailed overview of it.

By essence, an optimal control program (ocp) links two types of variables: the states (x) and the controls (u). Conceptually, the controls could be seen as the driving inputs of the system, which participate to changing the system states. In the case of biomechanics, the states (x) are usually the generalized coordinates (q) and velocities (qdot), i.e., the pose of the musculoskeletal model and the joint velocities. On the other hand, the controls (u) can be the generalized forces, i.e., the joint torques, but can also be the muscle excitations, for instance. States and controls are linked through Ordinary differential equations of the form: dx/dt = f(x, u, p), where p can be additional parameters that act on the system, but are not time dependent.

The following section investigate how to instruct bioptim of the dynamic equations the system should follow.

This class is the main class to define a dynamics. It therefore contains all the information necessary to configure (i.e., determining which variables are states or controls) and perform the dynamics. When constructing an OptimalControlProgram(), Dynamics is the expected class for the dynamics parameter.

The user can minimally define a Dynamics as follows: dyn = Dynamics(DynamicsFcn). The DynamicsFcn are the one presented in the corresponding section below.

The full signature of Dynamics is as follows:

Dynamics(dynamics_type, configure: Callable, dynamic_function: Callable, phase: int)

The dynamics_type is the selected DynamicsFcn. It automatically defines both configure and dynamic_function. If a function is sent instead, this function is interpreted as configure and the DynamicsFcn is assumed to be DynamicsFcn.CUSTOM If one is interested in changing the behaviour of a particular DynamicsFcn, they can refer to the Custom dynamics functions right below.

The phase is the index of the phase the dynamics applies to. This is usually taken care by the add() method of DynamicsList, but it can be useful when declaring the dynamics out of order.

If an advanced user wants to define their own dynamic function, they can define the configuration and/or the dynamics.

The configuration is what tells bioptim which variables are states and which are control. The user is expected to provide a function handler with the follow signature: custom_configure(ocp: OptimalControlProgram, nlp: NonLinearProgram). In this function the user is expected to call the relevant ConfigureProblem class methods:

• configure_q(nlp, as_states: bool, as_controls: bool)
• configure_qdot(nlp, as_states: bool, as_controls: bool)
• configure_q_qdot(nlp, as_states: bool, as_controls: bool)
• configure_tau(nlp, as_states: bool, as_controls: bool)
• configure_muscles(nlp, as_states: bool, as_controls: bool) where as_states add the variable to the states vector and as_controls to the controls vector. Please note that this is not necessary mutually exclusive. Finally, the user is expected to configure the dynamic by calling ConfigureProblem.configure_dynamics_function(ocp, nlp, custom_dynamics)

Defining the dynamic function must be done when one provides a custom configuration, but can also be defined by providing a function handler to the dynamic_function parameter for Dynamics. The signature of this custom dynamic function is as follows: custom_dynamic(states: MX, controls: MX, parameters: MX, nlp: NonLinearProgram. This function is expected to return a tuple[MX] of the derivative of the states. Some method defined in the class DynamicsFunctions can be useful, but will not be covered here since it is initially designed for internal use. Please note that MX type is a CasADi type. Anyone who wants to define custom dynamics should be at least familiar with this type beforehand.

A DynamicsList is simply a list of Dynamics. The add() method can be called exactly as if one was calling the Dynamics constructor. If the add() method is used more than one, the phase parameter is automatically incremented.

So a minimal use is as follows:

dyn_list = DynamicsList()
dyn_list.add(DynamicsFcn)

The DynamicsFcn class is the configuration and declaration of all the already available dynamics in bioptim. Since this is an Enum, it is possible to use tab key on the keyboard to dynamically list them all, depending on the capabilities of your IDE.

Please note that one can change the dynamic function associated to any of the configuration by providing a custom dynamics_function. For more information on this, please refer to the Dynamics and DynamicsList section right before.

The torque driven defines the states (x) as q and qdot and the controls (u) as tau. The derivative of q is trivially qdot. The derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau). If external forces are provided, they are added to the ForwardDynamics function.

The torque driven defines the states (x) as q and qdot and the controls (u) as tau. The derivative of q is trivially qdot. The derivative of qdot is given by the biorbd function that includes non-acceleration contact point defined in the bioMod: qddot = biorbd_model.ForwardDynamicsConstraintsDirect(q, qdot, tau).

The torque derivative driven defines the states (x) as q, qdot, tau and the controls (u) as taudot. The derivative of q is trivially qdot. The derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau). The derivative of tau is trivially taudot. If external forces are provided, they are added to the ForwardDynamics function.

The torque derivative driven defines the states (x) as q, qdot, tau and the controls (u) as taudot. The derivative of q is trivially qdot. The derivative of qdot is given by the biorbd function that includes non-acceleration contact point defined in the bioMod: qddot = biorbd_model.ForwardDynamicsConstraintsDirect(q, qdot, tau). The derivative of tau is trivially taudot.

The torque driven defines the states (x) as q and qdot and the controls (u) as the level of activation of tau. The derivative of q is trivially qdot. The actual tau is computed from the activation by the biorbd function: tau = biorbd_model.torque(torque_act, q, qdot). Then, the derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau).

Please note, this dynamics is expected to be very slow to converge, if it ever does. One is therefore encourage using TORQUE_DRIVEN instead, and to add the TORQUE_MAX_FROM_ACTUATORS constraint. This has been shown to be more efficient and allows defining minimum torque.

The torque driven defines the states (x) as q and qdot and the controls (u) as the level of activation of tau. The derivative of q is trivially qdot. The actual tau is computed from the activation by the biorbd function that includes non-acceleration contact point defined in the bioMod: tau = biorbd_model.torque(torque_act, q, qdot). Then, the derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau).

Please note, this dynamics is expected to be very slow to converge, if it ever does. One is therefore encourage using TORQUE_DRIVEN instead, and to add the TORQUE_MAX_FROM_ACTUATORS constraint. This has been shown to be more efficient and allows defining minimum torque.

The torque driven defines the states (x) as q and qdot and the controls (u) as the muscle activations. The derivative of q is trivially qdot. The actual tau is computed from the muscle activation converted in muscle forces and thereafter converted to tau by the biorbd function: biorbd_model.muscularJointTorque(muscles_states, q, qdot). The derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau).

The torque driven defines the states (x) as q and qdot and the controls (u) as the tau and the muscle activations (a). The derivative of q is trivially qdot. The actual tau is computed from the sum of tau to the muscle activation converted in muscle forces and thereafter converted to tau by the biorbd function: biorbd_model.muscularJointTorque(a, q, qdot). The derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau).

The torque driven defines the states (x) as q and qdot and the controls (u) as the tau and the muscle activations (a). The derivative of q is trivially qdot. The actual tau is computed from the sum of tau to the a converted in muscle forces and thereafter converted to tau by the biorbd function: biorbd_model.muscularJointTorque(a, q, qdot). The derivative of qdot is given by the biorbd function that includes non-acceleration contact point defined in the bioMod: qddot = biorbd_model.ForwardDynamics(q, qdot, tau).

The torque driven defines the states (x) as q, qdot and muscle activations (a) and the controls (u) as the EMG. The derivative of q is trivially qdot. The actual tau is computed from a converted in muscle forces and thereafter converted to tau by the biorbd function: biorbd_model.muscularJointTorque(muscles_states, q, qdot). The derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau). The derivative of a is computed by the biorbd function: adot = model.activationDot(emg, a)

The torque driven defines the states (x) as q, qdot and muscle activations (a) and the controls (u) as the tau and the EMG. The derivative of q is trivially qdot. The actual tau is computed from the sum of tau to a converted in muscle forces and thereafter converted to tau by the biorbd function: biorbd_model.muscularJointTorque(muscles_states, q, qdot). The derivative of qdot is given by the biorbd function: qddot = biorbd_model.ForwardDynamics(q, qdot, tau). The derivative of a is computed by the biorbd function: adot = model.activationDot(emg, a)

The torque driven defines the states (x) as q, qdot and muscle activations (a) and the controls (u) as the tau and the EMG. The derivative of q is trivially qdot. The actual tau is computed from the sum of tau to a converted in muscle forces and thereafter converted to tau by the biorbd function: biorbd_model.muscularJointTorque(muscles_states, q, qdot). The derivative of qdot is given by the biorbd function that includes non-acceleration contact point defined in the bioMod: qddot = biorbd_model.ForwardDynamics(q, qdot, tau). The derivative of a is computed by the biorbd function: adot = model.activationDot(emg, a)

This leaves the user to define both the configuration (what are the states and controls) and to define the dynamic function. CUSTOM should not be called by the user, but the user should pass the configure_function directly. You can have a look at Dynamics and DynamicsList sections for more information about how to configure and define custom dynamics.

The bounds provide a class that has minimal and maximal values for a variable. It is, for instance, useful for the inequality constraints that limit the maximal and minimal values of the states (x) and the controls (u) . In that sense, it is what is expected by the OptimalControlProgram for its u_bounds and x_bounds parameters. It can however be used for much more.

The Bounds class is the main class to define bounds. The constructor can be called by sending two boundary matrices (min, max) as such: bounds = Bounds(min_bounds, max_bounds). Or by providing a previously declared bounds: bounds = Bounds(bounds=another_bounds). The min_bounds and max_bounds matrices must have dimensions that fit the chosen InterpolationType, the default type being InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, which is 3 columns.

The full signature of Bounds is as follows:

Bounds(min_bounds, max_bound, interpolation: InterpolationType, phase: int)

The first parameters are presented before. The phase is the index of the phase the bounds apply to. This is usually taken care by the add() method of BoundsList, but it can be useful when declaring the bounds out of order.

If the interpolation type is CUSTOM, then the bounds are function handlers of signature:

custom_bound(current_shooting_point: int, n_elements: int, n_shooting: int)

where current_shooting_point is the current point to return, n_elements is the number of expected lines and n_shooting is the number of total shooting point (that is if current_shooting_point == n_shooting, this is the end of the phase)

The main methods the user will be interested in is the min property that returns the minimal bounds and the max property that returns the maximal bounds. Unless it is a custom function, min and max are numpy.ndarray and can be directly modified to change the boundaries. It is also possible to change min and max simultaneously by directly slicing the bounds as if it was a numpy.array, effectively defining an equality constraint: for instance bounds[:, 0] = 0. Finally, the concatenate(another_bounds: Bounds) method can be called to vertically concatenate multiple bounds.

A BoundsList is simply a list of Bounds. The add() method can be called exactly as if one was calling the Bounds constructor. If the add() method is used more than once, the phase parameter is automatically incremented.

So a minimal use is as follows:

bounds_list = BoundsList()
bounds_list.add(min_bounds, max_bounds)

The QAndQDotBounds is a Bounds that uses a biorbd_model to define the minimal and maximal bounds for the generalized coordinates (q) and velocities (qdot). It is particularly useful when declaring the states bounds for q and qdot. Anything that was presented for Bounds, also applies to QAndQDotBounds

The initial conditions the solver should start from, i.e., initial values of the states (x) and the controls (u). In that sense, it is what is expected by the OptimalControlProgram for its u_init and x_init parameters.

The InitialGuess class is the main class to define initial guesses. The constructor can be called by sending one initial guess matrix (init) as such: bounds = InitialGuess(init). The init matrix must have the dimensions that fits the chosen InterpolationType, the default type being InterpolationType.CONSTANT, which is 1 column.

The full signature of Bounds is as follows:

Bounds(initial_guess, interpolation: InterpolationType, phase: int)

The first parameters are presented before. The phase is the index of the phase the initial guess applies to. This is usually taken care by the add() method of InitialGuessList, but it can be useful when declaring the initial guess out of order.

If the interpolation type is CUSTOM, then the InitialGuess is a function handler of signature:

custom_bound(current_shooting_point: int, n_elements: int, n_shooting: int)

where current_shooting_point is the current point to return, n_elements is the number of expected lines and n_shooting is the number of total shooting point (that is if current_shooting_point == n_shooting, this is the end of the phase)

The main methods the user will be interested in is the init property that returns the initial guess. Unless it is a custom function, init is a numpy.ndarray and can be directly modified to change the initial guess. Finally, the concatenate(another_initial_guess: InitialGuess) method can be called to vertically concatenate multiple initial guesses.

A InitialGuessList is a list of InitialGuess. The add() method can be called exactly as if one was calling the InitialGuess constructor. If the add() method is used more than one, the phase parameter is automatically incremented.

So a minimal use is as follows:

init_list = InitialGuessList()
init_list.add(init)

The constraints are hard penalties of the optimization program. That means the solution won't be considered optimal unless all the constraint set is fully respected. The constraints come in two format: equality and inequality.

The Constraint provides a class that prepares a constraint, so it can be added to the constraint set by bioptim. When constructing an OptimalControlProgram(), Constraint is the expected class for the constraint parameter. It is also possible to later change the constraint by calling the method update_constraints(the_constraint) of the OptimalControlProgram

The Constraint class is the main class to define constraints. The constructor can be called with the type of the constraint and the node to apply it to, as such: constraint = Constraint(ConstraintFcn, node=Node.END). By default, the constraint will be an equality constraint equals to 0. To change this behaviour, one can add the parameters min_bound and max_bound to change the bounds to their desired values.

The full signature of Constraint is as follows:

Constraint(ConstraintFcn, node: node, index: list, phase: int, list_index: int, target: np.ndarray **extra_param)

The first parameters are presented before. The list is the list of elements to keep. For instance, if one defines a TRACK_STATE constraint with index=0, then only the first state is tracked. The default value is all the elements. The phase is the index of the phase the constraint should apply to. If it is not sent, phase=0 is assumed. The list_index is the ith element of a list for a particular phase This is usually taken care by the add() method of ConstraintList, but it can be useful when declaring the constraints out of order, or when overriding previously declared constraints using update_constraints. The target is a value subtracted to the constraint value. It is useful to define tracking problems. The dimensions of the target must be of [index, node]

The ConstraintFcn class provides a list of some predefined constraint functions. Since this is an Enum, it is possible to use tab key on the keyboard to dynamically list them all, assuming you IDE allows for it. It is possible however to define a custom constraint by sending a function handler in place of the ConstraintFcn. The signature of this custom function is: custom_function(pn: PenaltyNodeList, **extra_params) The PenaltyNodeList contains all the required information to act on the states and controls at all the nodes defined by node, while **extra_params are all the extra parameters sent to the Constraint constructor. The function is expected to return an MX vector of the constraint to be inside min_bound and max_bound. Please note that MX type is a CasADi type. Anyone who wants to define custom constraint should be at least familiar with this type beforehand.

A ConstraintList is by essence simply a list of Constraint. The add() method can be called exactly as if one was calling the Constraint constructor. If the add() method is used more than one, the list_index parameter is automatically incremented for the prescribed phase. If no phase are prescribed by the user, the first phase is assumed.

So a minimal use is as follows:

constraint_list = ConstraintList()
constraint_list.add(constraint)

The ConstraintFcn class is the declaration of all the already available constraints in bioptim. Since this is an Enum, it is possible to use tab key on the keyboard to dynamically list them all, depending on the capabilities of your IDE.

Tracks the states variable towards a target

Tracks the skin markers towards a target. The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Tracks the skin marker velocities towards a target.

Matches one marker with another one. The extra parameters first_marker_idx: int and second_marker_idx: int informs which markers are to be superimposed

Links one state to another, such that x[first_dof] = coef * x[second_dof] The extra parameters first_dof: int and second_dof: int must be passed to the Constraint constructor

Links one control to another, such that u[first_dof] = coef * u[second_dof] The extra parameters first_dof: int and second_dof: int must be passed to the Constraint constructor

Tracks the generalized forces part of the control variables towards a target

Tracks the muscles part of the control variables towards a target

Tracks all the control variables towards a target

Tracks the non-acceleration point reaction forces towards a target

Links a segment with an RT (for instance, an Inertial Measurement Unit). It does so by computing the homogenous transformation between the segment and the RT and then converting this to Euler angles. The extra parameters segment_idx: int and rt_idx: int must be passed to the Constraint constructor

Tracks a marker using a segment, that is aligning an axis toward the marker. The extra parameters marker_idx: int, segment_idx: int and axis: Axis must be passed to the Constraint constructor

Constraints the center of mass towards a target. The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Constraints the center of mass velocity towards a target. The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Adds a constraint to the non-acceleration point reaction forces. It is usually used in conjunction with changing the bounds, so it creates an inequality constraint on this contact force. The extra parameter contact_force_idx: int must be passed to the Constraint constructor

Adds a constraint of static friction at contact points constraining for small tangential forces. This constraint assumes that the normal forces is positive (that is having an additional CONTACT_FORCE with max_bound=np.inf). The extra parameters tangential_component_idx: int, normal_component_idx: int and static_friction_coefficient: float must be passed to the Constraint constructor

Adds a constraint of maximal torque to the generalized forces controls such that the maximal tau are computed from the biorbd method biorbd_model.torqueMax(q, qdot). This is an efficient alternative to the torque activation dynamics. The extra parameter min_torque can be passed to ensure that the model is never too weak

Adds the time to the optimization variable set. It will leave the time free, within the given boundaries

CUSTOM should not be directly sent by the user, but the user should pass the custom_constraint function directly. You can have a look at Constraint and ConstraintList sections for more information about how to define custom constraints.

The objective functions are soft penalties of the optimization program. That means the solution tries to minimize the value as much as possible but won't complaint if it does a bad job at it. The objective functions come in two format: Lagrange and Mayer.

The Lagrange objective functions are integrated over the whole phase (actually over the selected nodes, which are usually Node.ALL). One should note that integration is not given by the dynamics function but by the rectangle approximation over a node.

The Mayer objective functions are values at a single node, usually the Node.LAST.

The Objective provides a class that prepares an objective function, so it can be added to the objective set by bioptim. When constructing an OptimalControlProgram(), Objective is the expected class for the objective_functions parameter. It is also possible to later change the objective functions by calling the method update_objectives(the_objective_function) of the OptimalControlProgram

The Objective class is the main class to define objectives. The constructor can be called with the type of the objective and the node to apply it to, as such: objective = Objective(ObjectiveFcn, node=Node.END). Please note that ObjectiveFcn should either be a ObjectiveFcn.Lagrange or ObjectiveFcn.Mayer.

The full signature of Objective is as follows:

Objective(ObjectiveFcn, node: Node, index: list, phase: int, list_index: int, quadratic: bool, target: np.ndarray, weight: float, **extra_param)

The first parameters are presented before. The list is the list of elements to keep. For instance, if one defines a MINIMIZE_STATE objective_function with index=0, then only the first state is minimized. The default value is all the elements. The phase is the index of the phase the objective function should apply to. If it is not sent, phase=0 is assumed. The list_index is the ith element of a list for a particular phase This is usually taken care by the add() method of ObjectiveList, but it can be useful when declaring the objectives out of order, or when overriding previously declared objectives using update_objectives. quadratic is used to defined if the objective function should be squared. This is particularly useful when one wants to minimize toward 0 instead of minus infinity The target is a value subtracted to the objective value. It is useful to define tracking problems. The dimensions of the target must be of [index, node]. Finally, weight is the weighting that should be applied to the objective. The higher the weight is, the more important the objective is compared to the other objective functions.

The ObjectiveFcn class provides a list of some predefined objective functions. Since ObjectiveFcn.Lagrange and ObjectiveFcn.Mayer are Enum, it is possible to use tab key on the keyboard to dynamically list them all, assuming you IDE allows for it. It is possible however to define a custom objective function by sending a function handler in place of the ObjectiveFcn. If one do so, an additional parameter must be sent to the Objective constructor which is custom_type and must be either ObjectiveFcn.Lagrange or ObjectiveFcn.Mayer. The signature of the custom function is: custom_function(pn: PenaltyNodeList, **extra_params) The PenaltyNodeList contains all the required information to act on the states and controls at all the nodes defined by node, while **extra_params are all the extra parameters sent to the Objective constructor. The function is expected to return an MX vector of the objective function. Please note that MX type is a CasADi type. Anyone who wants to define custom objective functions should be at least familiar with this type beforehand.

An ObjectiveList is a list of Objective. The add() method can be called exactly as if one was calling the Objective constructor. If the add() method is used more than one, the list_index parameter is automatically incremented for the prescribed phase. If no phase are prescribed by the user, the first phase is assumed.

So a minimal use is as follows:

objective_list = ObjectiveList()
objective_list.add(objective)

Adds the time to the optimization variable set. It will try to minimize the time towards minus infinity or towards a target. If the Mayer term is used, min_bound and max_bound can also be defined.

Minimizes the states variable towards zero (or a target)

Tracks the states variable towards a target

Minimizes the position of the markers towards zero (or a target) The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Tracks the skin markers towards a target. The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Minimizes the difference between a state at a node and the same state at the next node, effectively minimizing the velocity The extra parameter coordinates_system_idx can be specified to compute the marker position in that coordinate system. Otherwise, it is computed in the global reference frame.

Minimizes the skin marker velocities towards zero (or a target)

Tracks the skin marker velocities towards a target.

Tracks one marker with another one. The extra parameters first_marker_idx: int and second_marker_idx: int informs which markers are to be superimposed

Minimizes the difference between one state and another, such that x[first_dof] ~= coef * x[second_dof] The extra parameters first_dof: int and second_dof: int must be passed to the Objective constructor

Minimizes the difference between one control and another, such that u[first_dof] ~= coef * u[second_dof] The extra parameters first_dof: int and second_dof: int must be passed to the Objective constructor

Minimizes the generalized forces part of the controls variable towards zero (or a target)

Tracks the generalized forces part of the controls variable towards a target

Minimizes the difference between a state at a node and the same state at the next node, effectively minimizing the generalized forces derivative

Minimizes the difference between a tau at a node and the same tau at the next node, effectively minimizing the generalized forces derivative

Minimizes the muscles part of the controls variable towards zero (or a target)

Tracks the muscles part of the controls variable towards a target

Minimizes all the controls variable towards zero (or a target)

Tracks all the controls variable towards a target

Minimizes the non-acceleration points reaction forces towards zero (or a target)

Tracks the non-acceleration points reaction forces towards a target

Minimizes the center of mass position towards zero (or a target). The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Minimizes the center of mass velocity towards zero (or a target). The extra parameter axis_to_track: Axis = (Axis.X, Axis.Y, Axis.Z) can be sent to specify the axes on which to track the markers

Minimizes the prediction of the center of mass maximal height from the parabolic equation, assuming vertical axis is Z (2): CoM_dot[2]**2 / (2 * -g) + CoM[2]. To maximize a jump, one can use this function at the end of the push-off phase and declare a weight of -1.

Minimizes the distance between a segment and an RT (for instance, an Inertial Measurement Unit). It does so by computing the homogenous transformation between the segment and the RT and then converting this to Euler angles. The extra parameters segment_idx: int and rt_idx: int must be passed to the Objective constructor

Minimizes the distance between a marker and an axis of a segment, that is aligning an axis toward the marker. The extra parameters marker_idx: int, segment_idx: int and axis: Axis must be passed to the Objective constructor

CUSTOM should not be directly sent by the user, but the user should pass the custom_objective function directly. You can have a look at Objective and ObjectiveList sections for more information about how to define custom objective function.

Parameters are time independent variables. It can be, for instance, the maximal value of the strength of a muscle, or even the value of gravity. If affects the dynamics of the whole system. Due to the variety of parameters, it was impossible to provide predefined parameters, apart from time. Therefore, all the parameters are custom made.

The ParameterList provides a class that prepares the parameters, so it can be added to the parameter set to optimize by bioptim. When constructing an OptimalControlProgram(), ParameterList is the expected class for the parameters parameter. It is also possible to later change the parameters by calling the method update_parameters(the_parameter_list) of the OptimalControlProgram

The ParameterList class is the main class to define parameters. Please note that unlike other lists, Parameter is not accessible, this is for simplicity reasons as it would complicate the API quite a bit to permit it. Therefore, one should not call the Parameter constructor directly.

Here is the full signature of the add() method of the ParameterList:

ParameterList.add(parameter_name: str, function: Callable, initial_guess: InitialGuess, bounds: Bounds, size: int, phase: int, penalty_list: Objective, **extra_parameters)

The parameter_name is the name of the parameter. This is how it will be referred to in the output data as well. The function is the function that modifies the biorbd model, it will be called just prior to applying the dynamics The signature of the custom function is: custom_function(biorbd.Model, MX, **extra_parameters), where biorbd.Model is the model to apply the parameter to, the MX is the value the parameter will take, and the **extra_parameters are those sent to the add() method. This function is expected to modify the biorbd_model, and not return anything. Please note that MX type is a CasADi type. Anyone who wants to define custom parameters should be at least familiar with this type beforehand. The initial_guess is the initial values of the parameter. The bounds are the maximal and minimal values of the parameter. The size is the number of element of this parameter. If an objective function is provided, the return of the objective function should match the size. The phase that the parameter applies to. Even though a parameter is time independent, one biorbd_model is loaded per phase. Since parameters are associate to a specific biorbd_model, one must define a parameter per phase. The penalty_list is the index in the list the penalty is. If one adds multiple parameters, the list is automatically incremented. It is useful however to define this value by hand if one wants to declare the parameters out of order or to override a previously declared parameter using update_parameters.

Bioptim can declare multiphase optimisation programs. The goal of a multiphase ocp is usually to handle changing dynamics. The user must understand that each phase is therefore a full ocp by itself, with constraints that links the end of which with the beginning of the following. Due to some limitations created by the use of MX variables, some things can be done and some cannot during a phase transition.

The PhaseTransitionList provide a class that prepares the phase transitions. When constructing an OptimalControlProgram(), PhaseTransitionList is the expected class for the phase_transitions parameter.

The PhaseTransitionList class is the main class to define parameters. Please note that unlike other lists, PhaseTransition is not accessible since phase transition don't make sense for single phase ocp. Therefore, one should not call the PhaseTransition constructor directly.

Here is the full signature of the add() method of the PhaseTransitionList:

PhaseTransitionList.add(PhaseTransitionFcn, phase_pre_idx, **extra_parameters)

The PhaseTransitionFcn is transition phase function to use. The default is CONTINUOUS. If one wants to declare a custom transition phase, then PhaseTransitionFcn is the function handler to the custom function. The signature of the custom function is: custom_function(state_pre: MX, state_post: MX, **extra_parameters), where state_pre is the states variable at the end of the phase before the transition, state_post is those at the beginning of the phase after the transition, and the **extra_parameters are those sent to the add() method. This function is expected to return the cost of the phase transition computed from the states pre and post in the form of an MX. Please note that MX type is a CasADi type. Anyone who wants to define phase transitions should be at least familiar with this type beforehand. The phase_pre_idx is the index of the phase before the transition.

The PhaseTransitionFcn class is the already available phase transitions in bioptim. Since this is an Enum, it is possible to use tab key on the keyboard to dynamically list them all, depending on the capabailities of your IDE.

The states at the end of the phase_pre equals the states at the beginning of the phase_post

The impulse function of biorbd: qdot_post = biorbd_model.ComputeConstraintImpulsesDirect, q_pre, qdot_pre) is apply to compute the velocities of the joint post impact. These computed states at the end of the phase_pre equals the states at the beginning of the phase_post.

If a bioMod with more contact points than the phase before is used, then the IMPACT transition phase should be used as well

Apply the CONTINUOUS phase transition to the end of the last phase and the begininning the of first, effectively creating a cyclic movement

CUSTOM should not be directly sent by the user, but the user should pass the custom_transition function directly. You can have a look at the PhaseTransitionList section for more information about how to define custom transition function.

Bioptim offers different ways to manage and visualize the results from an optimisation. This section explores the different methods that can be called to have a look at your data.

Everything related to managing the results can be accessed from the solution class returned from

sol = ocp.solve()

The Solution structure holds all the optimized values. To get the states variable, one can invoke the states = sol.states property. Similarly, to get the controls variable, one can invoke the states = sol.controls property. If the program was a single phase problem, then the returned values are dictionaries, otherwise it is a list of dictionaries of size equals to the number of phases. The keys of the returned dictionaries correspond to the name of the variables. For instance, if generalized coordinates (q) are states, then the state dictionary has q as key. In any cases, the key all is always there. The values inside the dictionaries are np.array of dimension n_elements x n_shooting, unless the data were previously altered by integrating or interpolating (then the number of columns may differ).

The parameters are very similar, but differs by the fact that it is always a dictionary (since parameters don't depend on the phases). Also, the values inside the dictionaries are of dimension n_elements x 1.

It is possible to integrate (also called simulate) the states at will, by calling the sol.integrate() method. The shooting_type: Shooting parameter allows to select the type of integration to perform (see the enum Shooting for more detail). The keepdims parameter allows to keep the initial dimensions of the return structure. If set to false, depending on the integrator, intermediate points between the node can be added (usually a multiple of n_steps of the Runge-Kutta). By definition, setting keepdims to True while asking for Shooting.MULTIPLE would return the exact same structure. This will therefore raise an error. The same is true if continusous is set to False, which necessarily changes the dimension. It is therefore prohibited. The merge_phase: bool parameter requests to merge all the phases into one [True] or not [False]. The continuous: bool parameter can be deceiving. If it mostly for internal purposes. In brief, it discards [True] or keeps [False] the arrival value of a node of integration, resulting in doubling the number of points at each node. Most of the time, one wants to set continuous to True, unless you need to get the individual integrations of each node.

The sol.interpolation(n_frames: [int, tuple]) method returns the states interpolated by changing the number of shooting points. If the program is multiphase, but only a int is sent, then the phases are merged and the interpolation keeps their respective time ratio consistent. If one does not want to merge the phases, then a tuple with one value per phase can be sent.

Finally sol.merge_phases() returns a Solution structure with all the phases merged into one.

Please note that, apart from sol.merge_phases(), these data manipulation methods return an incomplete Solution structure. This structure can be used for further analyses, but cannot be used for visualization. If one wants to visualize integrated or interpolated data, they are required to use the corresponding parameters or the visualization method they use.

A first method to visualize the data is sol.graphs(). This method will spawn all the graphs associated with the ocp. This is the same method that is called by the online plotter. In order to add and modify plots, one should use the ocp.add_plot() method. By default, this graphs the states as multiple shootings. If one wants to simulate in single shooting, the option shooting_type=Shooting.SINGLE will do the trick.

A second one is sol.animate(). This method summons one or more bioviz figures (depending if phases were merged or not) and animates the model. Please note that despite bioviz best efforts, plotting a lot of meshing vertices in MX format is slow. So even though it is possible, it is suggested to animate without the bone meshing (by passing the parameter show_meshes=False) To do so, we strongly suggest saving the data and load them in an environment where bioptim is compiled with the Eigen backend, which will be much more efficient. If n_frames is set, an interpolation is performed, otherwise, the phases are merged if possible, so a single animation is shown. To prevent from the phase merging, one can set n_frames=-1.

In order to print the values of the objective functions and constraints, one can use the sol.print() method. If the parameter cost_type=CostType.OBJECTIVE is passed, only the values of each objective functions are printed. The same is true for the constraints with CostType.CONSTRAINTS. Please note that for readability purposes, this method prints the sum by phases for the constraints.

It was hard to categorize the remaining classes and enum. So I present them in bulk in this extra stuff section

The mapping are a way to link things stored in a list. For instance, lets consider these vectors: a = [0, 0, 0, 10, -9] and b = [10, 9]. Even though they are quite different, they share some common values. It it therefore possible to retrieve a from b, and conversely.

This is what the Mapping class does, for the rows of numpy arrays. So if one was to declare the following Mapping: b_from_a = Mapping([3, -4]). Then, assuming a is a numpy.ndarray column vector (a = np.array([a]).T), it would be possible to summon b from a like so:

b = b_from_a.map(a)

Note that the -4 opposed the forth value. Conversely, using the a_from_b = Mapping([None, None, None, 0, -1]) mapping, and assuming b is a numpy.ndarray column vector (b = np.array([b]).T), it would be possible to summon b from a like so:

a = a_from_b.map(b)

Note the None are replaced by zeros.

The BiMapping is no more no less than a list of two mappings that link two matrices both ways: BiMapping(a_to_b, b_to_a)

The node targets some specific nodes of the ocp or of a phase

The accepted values are:

• START: The first node
• MID: The middle node
• INTERMEDIATES: All the nodes but the first and the last one
• PENULTIMATE: The second to last node of the phase
• END: The last node
• ALL: All the nodes
• TRANSITION: The last node of a phase and the first node of the next phase

The ordinary differential equation (ode) solver to solve the dynamics of the system. The RK4 and RK8 are the one with the most options available. IRK is supposed to be a bit more robust, but may be slower too. CVODES is the one with the least options, since it is not in-house implemented.

The accepted values are:

• RK4: Runge-Kutta of the 4th order
• RK8: Runge-Kutta of the 8th order
• IRK: Implicit runge-Kutta
• CVODES: cvodes solver

The nonlinear solver to solve the whole ocp. Each solver has some requirements (for instance, ̀Acados necessitates that the graph is SX). Feel free to test each of them to see which one fits the best your needs. ̀Ipopt is a robust solver, that may be a bit slow though. ̀Acados on the other is a very fast solver, but is much more sensitive to the relative weightings of the objective functions and to the initial guess. It is perfectly designed for MHE and NMPC problems.

The accepted values are:

• ̀Ipopt
• ̀Acados

The type the controls are. Typically, the controls for an optimal control program are constant over the shooting intervals. However, one may wants to get non constant values. Bioptim has therefore implemented some other types of controls.

The accepted values are:

• CONSTANT: The controls remain constant over the interval. The number of control is therefore equals to the number of shooting point
• LINEAR_CONTINUOUS: The controls are linearly interpolated over the interval. Since they are continuous, the end of an interval corresponds to the beginning of the next. There is therefore number of shooting point + 1 controls.

When adding a plot, it is possible to change the aspect of it.

The accepted values are: PLOT: Normal plot that links the points. INTEGRATED: Plot that links the points within an interval, but is discrete between the end of an interval and the beginning of the next one. STEP: Step plot, constant over an interval

How a time dependent variable is interpolated. It is mostly used for phases time span. Therefore, first and last nodes refer to the first and last nodes of a phase

The accepted values are:

• CONSTANT: Requires only one column, all the values are equal during the whole period of time.
• CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT: Requires three columns. The first and last columns correspond to the first and last node, while the middle corresponds to all the other nodes.
• LINEAR: Requires two columns. It corresponds to the first and last node. The middle nodes are linearly interpolated to get their values.
• EACH_FRAME: Requires as many columns as there are nodes. It is not an interpolation per se, but it allows the user to specify all the nodes individually.
• SPLINE: Requires five columns. It performs a cubic spline to interpolate between the nodes.
• CUSTOM: User defined interpolation function

The type of integration to perform

• MULTIPLE: resets the state at each node
• SINGLE: resets the state at each phase
• SINGLE_CONTINUOUS: never resets the state. The behaviour of SINGLE and SINGLE_CONTINUOUS are the same for a single phase program

The type of cost

• OBJECTIVES: The objective functions
• CONSTRAINTS: The constraints
• ALL: All the previously described cost type

In this section, you will find the description of all the examples implemented with bioptim. They are ordered in separate files. Each subsection corresponds to the different files, dealing with different examples and topics. Please note that the examples from the paper (see Citing) can be found in this repo https://github.com/s2mLab/BioptimPaperExamples.

In this subsection, all the examples of the getting_started file are described.

This example is a trivial box sent upward. It is designed to investigate the different bounds one can define in bioptim. Therefore, it shows how one can define the bounds, that is the minimal and maximal values of the state and control variables.

All the types of interpolation are shown : CONSTANT, CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, LINEAR, EACH_FRAME, SPLINE, and CUSTOM.

When the CUSTOM interpolation is chosen, the functions custom_x_bounds_min and custom_x_bounds_max are used to provide custom x bounds. The functions custom_u_bounds_min and custom_u_bounds_max are used to provide custom u bounds. In this particular example, one mimics linear interpolation using these four functions.

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. It is designed to show how one can define its own custom constraints function if the provided ones are not sufficient.

More specifically this example reproduces the behavior of the SUPERIMPOSE_MARKERS constraint.

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. It is designed to show how one can define its own custom dynamics function if the provided ones are not sufficient.

More specifically this example reproduces the behavior of the DynamicsFcn.TORQUE_DRIVEN using a custom dynamics.

The custom_dynamic function is used to provide the derivative of the states. The custom_configure function is used to tell the program which variables are states and controls.

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. It is designed to investigate the different way to define the initial guesses at each node sent to the solver.

All the types of interpolation are shown : CONSTANT, CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, LINEAR, EACH_FRAME, SPLINE, and CUSTOM.

When the CUSTOM interpolation is chosen, the custom_init_func function is used to custom the initial guesses of the states and controls. In this particular example, one mimics linear interpolation.

This example is a trivial box that tries to superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. It is designed to show how one can define its own custom objective function if the provided ones are not sufficient.

More specifically this example reproduces the behavior of the Mayer.SUPERIMPOSE_MARKERS objective function.

This example is closed to the example of the custom_constraint.py file. We use the custom_func_track_markers to define the objective function. In this example, one mimics the ObjectiveFcn.SUPERIMPOSE_MARKERS.

This example is a clone of the pendulum.py example with the difference that the model now evolves in an environment where the gravity can be modified. The goal of the solver it to find the optimal gravity (target = 8 N/kg), while performing the pendulum balancing task.

It is designed to show how one can define its own parameter objective functions if the provided ones are not sufficient.

The my_parameter_function function is used if one wants to modify the dynamics. In our case, we want to optimize the gravity. This function is called right before defining the dynamics of the system. The my_target_function function is a penalty function. Both these functions are used to define a new parameter, and then a parameter objective function linked to this new parameter.

This example is a trivial multiphase box that must superimpose different markers at beginning and end of each phase with one of its corner It is designed to show how one can define its phase transition constraints if the provided ones are not sufficient.

More specifically, this example mimics the behaviour of the most common PhaseTransitionFcn.CONTINUOUS

The custom_phase_transition function is used to define the constraint of the transition to apply. This function can be used when adding some phase transitions in the list of phase transitions.

Different phase transisitions can be considered. By default, all the phase transitions are continuous. However, in the event that one or more phase transitions is desired to be continuous, it is posible to define and use a function like the custom_phase_transition function, or directly use PhaseTransitionFcn.IMPACT. If a phase transition is desired between the last and the first phase, use the dedicated PhaseTransitionFcn.Cyclic.

This example is a trivial example using the pendulum without any objective. It is designed to show how to create new plots and how to expand pre-existing one with new information.

We define the custom_plot_callback function, which returns the value(s) to plot. We use this function as an argument of ocp.add_plot. Let's describe the creation of the plot "My New Extra Plot". custom_plot_callback takes two arguments, x and the array [0, 1, 3], as you can see below :

ocp.add_plot("My New Extra Plot", lambda x, u, p: custom_plot_callback(x, [0, 1, 3]), plot_type=PlotType.PLOT)

We use the plot_type PlotType.PLOT. This is a way to plot the first, second, and fourth states (ie. q_Seg1_TransY, q_Seg1_RotX and qdot_Seg1_RotX) in a new window entitled "My New Extra Plot". Please note that for further information about the different plot types, you can refer to the section "Enum: PlotType".

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. Moreover, the movement must be cyclic, meaning that the states at the end and at the beginning are equal. It is designed to provide a comprehensible example of the way to declare a cyclic constraint or objective function

A phase transition loop constraint is treated as hard penalty (constraint) if weight is <= 0 [or if no weight is provided], or as a soft penalty (objective) otherwise, as shown in the example below :

phase_transitions = PhaseTransitionList()
if loop_from_constraint:
    phase_transitions.add(PhaseTransitionFcn.CYCLIC, weight=0)
else:
    phase_transitions.add(PhaseTransitionFcn.CYCLIC, weight=10000)

loop_from_constraint is a boolean. It is one of the parameters of the prepare_ocp function of the example. This parameter is a way to determine if the looping cost should be a constraint [True] or an objective [False].

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. While doing so, a force pushes the box upward. The solver must minimize the force needed to lift the box while reaching the marker in time. It is designed to show how to use external forces. An example of external forces that depends on the state (for example a spring) can be found at 'examples/torque_driven_ocp/spring_load.py'

Please note that the point of application of the external forces are defined in the bioMod file by the externalforceindex tag in segment and is acting at the center of mass of this particular segment. Please note that this segment must have at least one degree of freedom defined (translations and/or rotations). Otherwise, the external_force is silently ignored.

Bioptim expects external_forces to be a list (one element for each phase) of np.array of shape (6, i, n), where the 6 components are [Mx, My, Mz, Fx, Fy, Fz], for the ith force platform (defined by the externalforceindex) for each node n. Let's take a look at the definition of the external forces in this example :

external_forces = [
    np.repeat(np.array([[0, 0, 0, 0, 0, -2], [0, 0, 0, 0, 0, 5]]).T[:, :, np.newaxis], n_shooting, axis=2)]

external_forces is of len 1 because there is only one phase. The array inside it is 6x2x30 since there is [Mx, My, Mz, Fx, Fy, Fz] for the two externalforceindex for each node (in this example, we take 30 shooting nodes).

This example mimics by essence what a jumper does which is maximizing the predicted height of the center of mass at the peak of an aerial phase. It does so with a very simple two segments model though. It is a clone of 'torque_driven_ocp/maximize_predicted_height_CoM.py' using the option MINIMIZE_PREDICTED_COM_HEIGHT. It is different in the sense that the contact forces on ground have to be downward (meaning that the object is limited to push on the ground, as one would expect when jumping, for instance).

Moreover, the lateral forces must respect some NON_SLIPPING constraint (that is the ground reaction forces have to remain inside of the cone of friction), as shown in the part of the code defining the constrainst:

constraints = ConstraintList()
   constraints.add(
   ConstraintFcn.CONTACT_FORCE,
   min_bound=min_bound,
   max_bound=max_bound,
   node=Node.ALL,
   contact_force_idx=1,
   )
constraints.add(
    ConstraintFcn.CONTACT_FORCE,
    min_bound=min_bound,
    max_bound=max_bound,
    node=Node.ALL,
    contact_force_idx=2,
    )
constraints.add(
    ConstraintFcn.NON_SLIPPING,
    node=Node.ALL,
    normal_component_idx=(1, 2),
    tangential_component_idx=0,
    static_friction_coefficient=mu,
    )

Let's describe the code above. First, we create a list of consraints. Then, two contact forces are defined, respectively with the indexes 1 and 2. The last step is the implementation of the non slipping constraint for the two forces defined before.

This example is designed to show how to use min_bound and max_bound values so they define inequality constraints instead of equality constraints, which can be used with any ConstraintFcn.

An example of mapping can be found at 'examples/symmetrical_torque_driven_ocp/symmetry_by_mapping.py'. Another example of mapping can be found at 'examples/getting_started/example_inequality_constraint.py'.

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and a the at different marker at the end of each phase. Moreover a constraint on the rotation is imposed on the cube. It is designed to show how one can define a multiphase optimal control program.

In this example, three phases are implemented. The long_optim boolean allows users to choose between solving the precise optimization or the approximate. In the first case, 500 points are considered and n_shooting = (100, 300, 100). Otherwise, 50 points are considered and n_shooting = (20, 30, 20). Three steps are necessary to define the objective functions, the dynamics, the constraints, the path constraints, the initial guesses and the control path contsraints. Each step corresponds to one phase.

Let's take a look at the definition of the constraints:

constraints = ConstraintList()
constraints.add(
    ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker_idx=0, second_marker_idx=1, phase=0
)
constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker_idx=0, second_marker_idx=2, phase=0)
constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker_idx=0, second_marker_idx=1, phase=1)
constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker_idx=0, second_marker_idx=2, phase=2)

First, we define a list of constraints, and then we add constraints to the list. At the beginning, marker 0 must superimpose marker 1. At the end of the first phase (the first 100 shooting nodes if we solve the precise optimization), marker 0 must superimpose marker 2. Then, at the end of the second phase, marker 0 must superimpose marker 1. At the end of the last step, marker 0 must superimpose marker 2. Please, note that the definition of the markers is implemented in the bioMod file corresponding to the model. Further information about the definition of the markers is available in the biorbd documentation.

Examples of time optimization can be found in 'examples/optimal_time_ocp/'.

This is a clone of the getting_started/pendulum.py example. It is designed to show how to create and solve a problem, and afterward, save it to the hard drive and reload it. It shows an example of *.bo method.

Let's take a look at the most important lines of the example. To save the optimal control program and the solution, use ocp.save(sol, "pendulum.bo"). To load the optimal control program and the solution, use ocp_load, sol_load = OptimalControlProgram.load("pendulum.bo"). Then, to show the results, simply use sol_load.animate().

The first part of this example of a single shooting simulation from initial guesses. It is not an optimal control program. It is merely the simulation of values, that is applying the dynamics. The main goal of this kind of simulation is to get a sens of the initial guesses passed to the solver.

The second part of the example is to actually solve the program and then simulate the results from this solution. The main goal of this kind of simulation, especially in single shooting (that is not resetting the states at each node) is to validate the dynamics of multiple shooting. If they both are equal, it usually means that a great confidence can be held in the solution. Another goal would be to reload fast a previously saved optimized solution.

This is another way to present the pendulum example of the 'Getting started' section.

In this file, you will find four examples about muscle driven optimal control programs. The two first refer to traking examples. The two last refer to reaching tasks.

This is an example of muscle activation/skin marker or state tracking. Random data are created by generating a random set of muscle activations and then by generating the kinematics associated with these data. The solution is trivial since no noise is applied to the data. Still, it is a relevant example to show how to track data using a musculoskeletal model. In real situation, the muscle activation and kinematics would indeed be acquired via data acquisition devices.

The difference between muscle activation and excitation is that the latter is the derivative of the former.

The generate_data function is used to create random data. First, a random set of muscle activation is generated, as shown below: U = np.random.rand(n_shooting, n_mus).T

Then, the kinematics associated with these data are generated by numerical integration, using scipy.integrate.solve_ivp.

To implement this tracking task, we use the ObjectiveFcn.Lagrange.TRACK_STATE objective function in the case of a state tracking, or the ObjectiveFcn.Lagrange.TRACK_MARKERS objective function in the case of a marker tracking. We also use the ObjectiveFcn.Lagrange.TRACK_MUSCLES_CONTROL objective function. The user can choose between marker or state tracking thanks to the string kin_data_to_track which is one of the prepare_ocp function parameters.

This is an example of muscle excitation(EMG)/skin marker or state tracking. Random data are created by generating a random set of EMG and then by generating the kinematics associated with these data. The solution is trivial since no noise is applied to the data. Still, it is a relevant example to show how to track data using a musculoskeletal model. In real world, the EMG and kinematics would indeed be acquired via data acquisition devices.

There is no huge difference with the precedent example. Some dynamic equations make the link between muscle activation and excitation.

This is a basic example on how to use biorbd model driven by muscle to perform an optimal reaching task. The arms must reach a marker placed upward in front while minimizing the muscles activity.

For this reaching task, we use the ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS objective function. At the end of the movement, marker 0 and marker 5 should superimpose. The weight applied to the SUPERIMPOSE_MARKERS objective function is 1000. Please note that the bigger this number, the greater the model will try to reach the marker.

Please note that using show_meshes=True in the animator may be long due to the creation of a huge CasADi graph of the mesh points.

This is a basic example on how to use biorbd model driven by muscle to perform an optimal reaching task with a contact dynamics. The arms must reach a marker placed upward in front while minimizing the muscles activity.

The only difference with the precedent example is that we use the arm26_with_contact.bioMod model and the DynamicsFcn.MUSCLE_ACTIVATIONS_AND_TORQUE_DRIVEN_WITH_CONTACT dynamics function instead of DynamicsFcn.MUSCLE_ACTIVATIONS_AND_TORQUE_DRIVEN.

Please note that using show_meshes=True in the animator may be long due to the creation of a huge CasADi graph of the mesh points.

All the examples in muscle_driven_with_contact are merely to show some dynamics and prepare some OCP for the tests. It is not really relevant and will be removed when unitary tests for the dynamics will be implemented.

In this example, we implement inequality constraints on two contact forces. It is designed to show how to use min_bound and max_bound values so they define inequality constraints instead of equality constraints, which can be used with any ConstraintFcn.

In this case, the dynamics function used is DynamicsFcn.MUSCLE_ACTIVATIONS_AND_TORQUE_DRIVEN_WITH_CONTACT.

In this example, we implement inequality constraints on two contact forces. It is designed to show how to use min_bound and max_bound values so they define inequality constraints instead of equality constraints, which can be used with any ConstraintFcn.

In this case, the dynamics function used is DynamicsFcn.MUSCLE_EXCITATIONS_AND_TORQUE_DRIVEN_WITH_CONTACT instead of DynamicsFcn.MUSCLE_ACTIVATIONS_AND_TORQUE_DRIVEN_WITH_CONTACT used in the precedent example.

In this example, we track both muscle controls and contact forces, as it is defined when adding the two objective functions below, using both ObjectiveFcn.Lagrange.TRACK_MUSCLES_CONTROL and ObjectiveFcn.Lagrange.TRACK_CONTACT_FORCES objective functions.

objective_functions = ObjectiveList()
objective_functions.add(ObjectiveFcn.Lagrange.TRACK_MUSCLES_CONTROL, target=muscle_activations_ref)
objective_functions.add(ObjectiveFcn.Lagrange.TRACK_CONTACT_FORCES, target=contact_forces_ref)

Let's take a look at the structure of this example. First, we load data to track, and we generate data using the data_to_track.prepare_ocp optimization control program. Then, we track these data using muscle_activation_ref and contact_forces_ref as shown below:

ocp = prepare_ocp(
    biorbd_model_path=model_path,
    phase_time=final_time,
    n_shooting=ns,
    muscle_activations_ref=muscle_activations_ref[:, :-1],
    contact_forces_ref=contact_forces_ref,
)

In this section, you will find four examples showing how to play with time parameters.

This example is a trivial multiphase box that must superimpose different markers at beginning and end of each phase with one of its corner. The time is free for each phase. It is designed to show how one can define a multi-phase ocp problem with free time.

In this example, the number of phases is 1 or 3. prepare_ocp function takes time_min, time_max and final_time as arguments. There are arrays of length 3 in the case of a 3-phase problem. In the example, these arguments are defined as shown below:

final_time = [2, 5, 4]
time_min = [1, 3, 0.1]
time_max = [2, 4, 0.8]
ns = [20, 30, 20]
ocp = prepare_ocp(final_time=final_time, time_min=time_min, time_max=time_max, n_shooting=ns)

We can make out different time constraints for each phase, as shown in the code below:

constraints.add(ConstraintFcn.TIME_CONSTRAINT, node=Node.END, min_bound=time_min[0], max_bound=time_max[0], phase=0)
if n_phases == 3:
    constraints.add(
        ConstraintFcn.TIME_CONSTRAINT, node=Node.END, min_bound=time_min[1], max_bound=time_max[1], phase=1
    )
    constraints.add(
        ConstraintFcn.TIME_CONSTRAINT, node=Node.END, min_bound=time_min[2], max_bound=time_max[2], phase=2
    )

This is a clone of the example/getting_started/pendulum.py where a pendulum must be balance. The difference is that the time to perform the task is now free and minimized by the solver, as shown in the definition of the objective function used for this example:

objective_functions = ObjectiveList()
objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TIME, weight=1)

Please note that a weight of -1 will maximize time.

This example shows how to define such an optimal control program with a Lagrange criteria (integral of dt).

The difference between Mayer and Lagrange minimization time is that the former can define bounds to the values, while the latter is the most common way to define optimal time.

This is a clone of the example/getting_started/pendulum.py where a pendulum must be balance. The difference is that the time to perform the task is now free and minimized by the solver, as shown in the definition of the objective function used for this example:

objective_functions = ObjectiveList()
objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_TIME, weight=weight, min_bound=min_time, max_bound=max_time)

Please note that a weight of -1 will maximize time.

This example shows how to define such an optimal control program with a Mayer criteria (value of final_time).

The difference between Mayer and Lagrange minimization time is that the former can define bounds to the values, while the latter is the most common way to define optimal time.

This is a clone of the example/getting_started/pendulum.py where a pendulum must be balance. The difference is that the time to perform the task is now free for the solver to change. This example shows how to define such an optimal control program.

In this example, a time constraint is implemented:

constraints = Constraint(ConstraintFcn.TIME_CONSTRAINT, node=Node.END, min_bound=time_min, max_bound=time_max)

In this section, you will find an example using symmetry by constraint and another using symmetry by mapping. In both cases, we simulate two rodes. We must superimpose a marker on one rod at the beginning and another marker on the same rod at the end, while keeping the degrees of freedom opposed.

The difference between the first example (symmetry_by_mapping) and the second one (symmetry_by_constraint) is that one (mapping) removes the degree of freedom from the solver, while the other (constraints) imposes a proportional constraint (equals to -1) so they are opposed. Please note that even though removing a degree of freedom seems a good idea, it is unclear if it is actually faster when solving with IPOPT.

This example imposes a proportional constraint (equals to -1) so that the rotation around the x axis remains opposed for the two rodes during the movement.

Let's take a look at the definition of such a constraint:

constraints.add(ConstraintFcn.PROPORTIONAL_STATE, node=Node.ALL, first_dof=2, second_dof=3, coef=-1)

In this case, a proportional constraint is generated between the third degree of freedom defined in the bioMod file (first_dof=2) and the fourth one (second_dof=3). Looking at the cubeSym.bioMod file used in this example, we can make out that the dof with index 2 corresponds to the rotation around the x axis for the first segment Seg1. The dof with index 3 corresponds to the rotation around the x axis for the second segment Seg2.

This example imposes the symmetry as a mapping, that is by completely removing the degree of freedom from the solver variables but interpreting the numbers properly when computing the dynamics.

A BiMapping is used. The way to understand the mapping is that if one is provided with two vectors, what would be the correspondence between those vector. For instance, BiMapping([None, 0, 1, 2, -2], [0, 1, 2]) would mean that the first vector (v1) has 3 components and to create it from the second vector (v2), you would do: v1 = [v2[0], v2[1], v2[2]]. Conversely, the second v2 has 5 components and is created from the vector v1 using: v2 = [0, v1[0], v1[1], v1[2], -v1[2]]. For the dynamics, it is assumed that v1 is what is to be sent to the dynamic functions (the full vector with all the degrees of freedom), while v2 is the one sent to the solver (the one with less degrees of freedom).

The BiMapping used is defined as a problem parameter, as shown below:

all_generalized_mapping = BiMapping([0, 1, 2, -2], [0, 1, 2])

In this section, you will find different examples showing how to implement torque driven optimal control programs.

This example mimics by essence what a jumper does which is maximizing the predicted height of the center of mass at the peak of an aerial phase. It does so with a very simple two segments model though. It is designed to give a sense of the goal of the different MINIMIZE_COM functions and the use of weight=-1 to maximize instead of minimizing.

Let's take a look at the definition of the objetive functions used for this example to better understand how to implement that:

objective_functions = ObjectiveList()
if objective_name == "MINIMIZE_PREDICTED_COM_HEIGHT":
    objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT, weight=-1)
elif objective_name == "MINIMIZE_COM_POSITION":
    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_COM_POSITION, axis=Axis.Z, weight=-1)
elif objective_name == "MINIMIZE_COM_VELOCITY":
    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_COM_VELOCITY, axis=Axis.Z, weight=-1)

Another interesting point of this example is the definition of the constraints. Thanks to the com_constraints boolean, the user can easily choose to apply constraints on the center of mass. Here is the definition of the constraints for our example:

constraints = ConstraintList()
if com_constraints:
    constraints.add(
        ConstraintFcn.TRACK_COM_VELOCITY,
        node=Node.ALL,
        min_bound=np.array([-100, -100, -100]),
        max_bound=np.array([100, 100, 100]),
    )
    constraints.add(
        ConstraintFcn.TRACK_COM_POSITION,
        node=Node.ALL,
        min_bound=np.array([-1, -1, -1]),
        max_bound=np.array([1, 1, 1]),
    )

This example is designed to show how to use min_bound and max_bound values so they define inequality constraints instead of equality constraints, which can be used with any ConstraintFcn. This example is closed to the example_inequality_constraint.py file you can find in 'examples/getting_started/example_inequality_constraint.py'.

This trivial spring example targets to have the highest upward velocity. It is however only able to load a spring by pulling downward and afterward to let it go so it gains velocity. It is designed to show how one can use the external forces to interact with the body.

This example is closed to the custom_dynamics.py file you can find in 'examples/getting_started/custom_dynamics.py'. Indeed, we generate an external force thanks to the custom_dynamic function. Then, we configure the dynamics with the custom_configure function.

This example uses the data from the balanced pendulum example to generate the data to track. When it optimizes the program, contrary to the vanilla pendulum, it tracks the values instead of 'knowing' that it is supposed to balance the pendulum. It is designed to show how to track marker and kinematic data.

Note that the final node is not tracked.

In this example, we use both ObjectiveFcn.Lagrange.TRACK_MARKERS and ObjectiveFcn.Lagrange.TRACK_TORQUE objective functions to track data, as shown in the definition of the objective functions used in this example:

objective_functions = ObjectiveList()
objective_functions.add(
    ObjectiveFcn.Lagrange.TRACK_MARKERS, axis_to_track=[Axis.Y, Axis.Z], weight=100, target=markers_ref
)
objective_functions.add(ObjectiveFcn.Lagrange.TRACK_TORQUE, target=tau_ref)

This is a good example of how to load data for tracking tasks, and how to plot data. The extra parameter axis_to_track allows users to specify the axes on which to track the markers (x and y axes in this example). This example is closed to the example_save_and_load.py and custom_plotting.py files you can find in the examples/getting_started repository.

This example is a trivial box that must superimpose one of its corner to a marker at the beginning of the movement and superimpose the same corner to a different marker at the end. It is a clone of 'getting_started/custom_constraint.py'

It is designed to show how to use the TORQUE_ACTIVATIONS_DRIVEN which limits the torque to [-1; 1]. This is useful when the maximal torque are not constant. Please note that this dynamic then to not converge when it is used on more complicated model. A solution that defines non-constant constraints seems a better idea. An example of which can be found with the bioptim paper.

Let's take a look at the structure of the code. First, tau_min, tau_max and tau_init are respectively initialized to -1, 1 and 0 if the integer actuator_type (which is a parameter of the prepare_ocp function) equals to 1. In this particular case, the dynamics function used is DynamicsFcn.TORQUE_ACTIVATIONS_DRIVEN.

This example uses a representation of a human body by a trunk_leg segment and two arms. It is designed to show how to use a model that has quaternions in their degrees of freedom.

In this section, you will find the description of two tracking examples.

This example is a trivial example where a stick must keep a corner of a box in line for the whole duration of the movement. The initial and final position of the box are dictated, the rest is fully optimized. It is designed to show how one can use the tracking function to track a marker with a body segment.

In this case, we use the ConstraintFcn.TRACK_MARKER_WITH_SEGMENT_AXIS constraint function, as shown below in the definition of the constraints of the problem:

constraints = ConstraintList()
constraints.add(
ConstraintFcn.TRACK_MARKER_WITH_SEGMENT_AXIS, node=Node.ALL, marker_idx=1, segment_idx=2, axis=Axis.X
)

Here, we minimize the distance between the marker with index 1 ans the x axis of the segment with index 2. We align the axis toward the marker.

This example is a trivial example where a stick must keep its coordinate system of axes aligned with the one from a box during the whole duration of the movement. The initial and final position of the box are dictated, the rest is fully optimized. It is designed to show how one can use the tracking RT function to track any RT (for instance Inertial Measurement Unit [IMU]) with a body segment.

To implement this tracking task, we use the ConstraintFcn.TRACK_SEGMENT_WITH_CUSTOM_RT constraint function, which minimizes the distance between a segment and an RT. The extra parameters segment_idx: int and rt_idx: int must be passed to the Objective constructor.

In this section, we perform mhe on the pendulum example.

In this example, mhe (Moving Horizon Estimation) is applied on a simple pendulum simulation. Data are generated (states, controls, and marker trajectories) to simulate the movement of a pendulum, using scipy.integrate.solve_ivp. These data are used to perform mhe.

In this example, 500 shooting nodes are defined. As the size of the mhe window is 10, 490 iterations are performed to solve the complete problem.

For each iteration, the new marker trajectory is taken into account so that a real time data acquisition is simulated. For each iteration, the list of objectives is updated, the problem is solved with the new frame added to the window, the oldest frame is discarded with the warm_start_mhe function, and it is saved. The results are plotted so that estimated data can be compared to real data.

In this section, you will find three examples to investigate bioptim using acados.

This is a basic example of a cube which have to reach a target at the end of the movement, starting from an initial position, and minimizing states and torques. This problem is solved using acados.

A very simple yet meaningful optimal control program consisting in a pendulum starting downward and ending upward while requiring the minimum of generalized forces. The solver is only allowed to move the pendulum sideways.

This simple example is a good place to start investigating bioptim using acados as it describes the most common dynamics out there (the joint torque driven), it defines an objective function and some boundaries and initial guesses.

This is a basic example on how to use biorbd model driven by muscle to perform an optimal reaching task. The arm must reach a marker while minimizing the muscles activity and the states. We solve the problem using both acados and ipotpt.

If you use bioptim, we would be grateful if you could cite it as follows: @article {Bioptim2021, author = {Michaud, Benjamin and Bailly, Fran{\c c}ois and Charbonneau, Eve and Ceglia, Amedeo and Sanchez, L{'e}a and Begon, Mickael}, title = {Bioptim, a Python framework for Musculoskeletal Optimal Control in Biomechanics}, elocation-id = {2021.02.27.432868}, year = {2021}, doi = {10.1101/2021.02.27.432868}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/10.1101/2021.02.27.432868v1}, eprint = {https://www.biorxiv.org/content/10.1101/2021.02.27.432868v1.full.pdf}, journal = {bioRxiv} }