Publication
BOLeRo: Behavior Optimization and Learning for Robots
Alexander Fabisch; Malte Langosz; Frank Kirchner
In: International Journal of Advanced Robotic Systems, Vol. 17, No. 3, Pages n.n.-n.n. SAGE Publications, 2020.
Abstract
Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because
algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art
algorithms are often not publicly available though and experiments are hardly reproducible because open source
implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible
to deploy these algorithms on robotic systems. BOLERO closes this gap for policy search and evolutionary algorithms
by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic
middlewares and it can be used to compare methods and develop prototypes in simulation.
Projects
- BesMan - BesMan - Behaviours for Mobile Manipulation
- LIMES - LIMES - Learning Intelligent Motions for Kinematically Complex Robots for Exploration in Space
- COROMA - Cognitively enhanced robot for flexible manufacturing of metal and composite parts
- Q-Rock - AI-based Qualification of Deliberative Behaviour for a Robotic Construction Kit
- D-Rock - Models, methods and tools for the model based software development of robots