Software tools

There are 9 software tools found.


Behavior Optimization and Learning for Robots
BOLeRo provides tools to learn behaviors for robots. That includes behavior representations as well as reinforcement learning, black-box optimization, and evolutionary algorithms and imitation learning. It provides a C++ and a Python interface to be efficient where this is required and to be flexible and convenient where performance is not an issue. Because the library provides a C++ interface, it is easy to integrate in most robotic frameworks, e.g. the robot operating system (ROS) or the robot construction kit (Rock).
Machine Learning, Evolutionary Computation, Behavior Learning, Optimization


Computer Aided Design To Simulation
The purpose of the program CAD-2-SIM is facilitating the transfer of mechanism specifications from computer aided design programs to simulation software. In particular, the program allows to export the numeric data that describe the kinematic and the dynamic properties of a mechanism without manual intervention from the CAD program to description formats of several simulation environments (Openrave, Mars, RBDL, ROS, ROCK, SimMechanics). By this direct data transformation, a faster and more stable development process is enabled. The program CAD-2-SIM uses the Sheth-Uicker convention and a graph-based enumeration scheme.
Kinematic-Dynamic Modeling, Mechanical Synthesis, Computer Aided Design


Machina Arte Robotum Simulans
MARS is a cross-platform simulation and visualisation tool created for robotics research. It consists of a core framework containing all main simulation components, a GUI (based on Qt), 3D visualization (using OSG) and a physics engine (based on ODE). MARS is designed in a modular manner and can be used very flexibly, e.g. by running the physics simulation without visualization and GUI. It is possible to extend MARS writing your own plugins and many plugins introducing various functionality such as HUDs or custom ground reaction forces already exist - and it's easy to write your own.
Simulation, Visualisierung


Maja Machine Learning Framework
The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.
Reinforcement learning, Machine learning, Evolutionary algorithms


Node Level Data Link Communication
Modern robotic systems come with a variety of decentralized sensor- and controlelektronics which frequently need to communicate with one another. The NDLCom protocol allows to exchange small packets of data between microcontrollers, FPGAs and a computer. Each participant is connected to one of its neighbours via a point-to-point connection and has to forward incoming messages according to the receiver address in the packet header. Implementations to receive, forward and decode messages exist in C/C++ and VHDL. The visualization, logging and exporting of received data is accomplished in a graphical user interfaces.
Serial communication, OSI-Layer, Embedded, C, VHDL, Qt, CSV-Export


An add-on for Blender allowing editing and exporting of robots for the MARS simulation
Creating adequate simulation models of a robot is a difficult task that especially in the world of open source and research oftentimes comes down to editing complex custom data files by hand. Phobos is an open-source Add-On for Blender designed to simplify this task, allowing the user to create robot models in a visual, interactive user interface and supporting their export as URDF files as well as SMURF robot descriptions for use with the MARS simulation.
Simulation, robot model, modelling


Signal Processing and Classification Environment written in Python
pySPACE is a modular software for processing segmented time series and feature vector data. It has been specifically designed to enable distributed execution and empirical evaluation of manifold signal processing chains and can be used for benchmarking and online applications. It automatically loads, processes, and stores different datasets. Signal processing algorithms (nodes) and larger transformations of several datasets (operations) can be easily concatenated. The automatic processing can be done in parallel on a multicore system or cluster. pySPACE provides a growing number of algorithms and is actively maintained and developed.
machine learning, signal processing, parallelisation


Reconfigurable Signal Processing and Classification Environment
The framework reSPACE (reconfigurable Signal Processing and Classification Environment) was developed to facilitate the development of application-specific FPGA-based hardware accelerators for embedded and mobile systems. respace uses a model based development process to accelerate the development of the accelerators. The focus lies especially on applications in the areas signal and image processing as well as machine learning.
Machine Learning, Signal Processing, Parallelization, FPGA, Embedded Systems


Robot Construction Kit
Rock is a software framework for the development of robotic systems. The underlying component model is based on the Orocos RTT (Real Time Toolkit). Rock provides all the tools required to set up and run high-performance and reliable robotic systems for wide variety of applications in research and industry. It contains a rich collection of ready to use drivers and modules for use in your own system, and can easily be extended by adding new components.
Robots, Framework, Components, Modular, Drivers, Real-time
last updated 03.01.2017
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