This is Humoto - a software framework for manipulation of linear least-squares problems with (in)equality constraints. It supports both weighting and lexicographic prioritization and can be characterized as a tool for goal programming. However, the development was driven by works in other fields - robotics, control, and numerical optimization; for this reason our terminology and interpretations are different.

The core functionalities of Humoto are formulation of least-squares problems and their resolution with the help of various third-party software using uniform programming interfaces. However, due to our interest in robotic applications, the framework also facilitates formulation and implementation of optimization problems for control of robots, in particular, model predictive control problems. For the same reason, we pay special attention to computational performance in order to be able to employ the framework in real-time scenarios.

In addition to the core components, the distribution of Humoto includes several modules - implementations of specific controllers. The modules serve as examples of using the framework, but can also be used in accordance with their primary purpose.

@tableofcontents

Humoto is distributed under the terms of Apache 2.0 license, see [LICENSE](@ref LICENSE) for more information. All files are covered by the same license except those which were borrowed from other projects as indicated in [INSTALL.md](@ref md_install) file.

The source code can be obtained using git:

• git clone https://github.com/bip-team/humoto

The framework is distributed with two kinds of documentation:

• PDF manual overviews the main concepts and contains mathematical derivations. If the precompiled version is not available, you have to go to 'doc/latex' and execute 'make' to compile it.

• Doxygen documentation for developers which incorporates a number of files in markdown format stored in the root and 'doc' directories. It can be compiled by calling 'make' in 'doc/doxygen' folder.

Precompiled documentation is available at https://bip-team.github.io/humoto/.

The first step is to read the introductory chapter of LaTeX documentation, then, depending on your interests, you may

1. learn about

   • the authors from [AUTHORS.md](@ref md_authors)
   • the directory layout from [doc/directory_layout.md](@ref md_directory_layout)
   • dependencies and compilation of tests from file [INSTALL.md](@ref md_install)

2. use one of the provided modules

   • you can find an example in [modules/wpg04/tests/test_000.cpp](@ref wpg04_example)
   • relevant mathematical derivations can be found in the respective chapters of LaTeX documentation

3. modify the core or a module

   • you should first read the coding policies in [doc/coding_policies.md](@ref md_coding_policies)

   • then you can learn how to

     • implement new tasks [doc/howto_tasks.md](@ref md_howto_tasks)

     • add support of configuration files to your classes in [doc/howto_configurable.md](@ref md_howto_configurable)

• Early versions of Humoto were used for walking control of HRP-4 humanoid robot. See module [wpg03](@ref wpg03), its LaTeX documentation and references therein, and a video of the robot during the trials.

• More recently, Humoto has been used for motion control of a humanoid robot on a wheeled base (Pepper). See modules [pepper_mpc](@ref pepper_mpc) and [pepper_ik](@ref pepper_ik) and their LaTeX documentation.

• RobOptim? Humoto and RobOptim were developed with one similar goal in mind: interfacing various optimization solvers in robot control applications. There are, however, many differences. Humoto cannot deal with general nonlinear problems as RobOptim does, but, unlike RobOptim, Humoto supports hierarchical (lexicographic) optimization problems. Moreover, our framework standardizes and automates formulation of optimization problems using the concept of '[task](@ref md_howto_tasks)'. Finally, we pay more attention to performance, e.g., Humoto supports more hot-starting options and provides [functionality](@ref etools) for blockwise matrix operations with sparsity support.

• SOTH (SOT Hierarchical solver )? Humoto is a framework which is designed to provide common interface to various solvers, including hierarchical (lexicographic) solvers, while SOTH is a particular hierarchical solver.

• Todo: see [doxygen todo page](@ref todo) and [doc/known_issues.md](@ref md_known_issues)