A Behavior-based Library for Locomotion Control of Kinematically Complex Robots
Malte Langosz, Sebastian Bartsch, Alexander Dettmann, Frank Kirchner, Lorenz Quack
In Proceedings of the 16th International Conference on Climbing and Walking Robots, (CLAWAR-2013), 14.7.-17.7.2013, Sydney, NSW, o.A., Jul/2013.

Abstract :

Controlling the locomotion of kinematically complex robots is a challenging task because different control approaches are needed to operate safely and efficiently in changing environments. This paper presents a graph-based behavior description which allows to dynamically replace behaviors on a robotic system. In the proposed approach, every behavior is represented as a directed graph that can be encoded into a data block which can be saved to or loaded from a behavior library. Since this is not a precompiled module like in other systems, the algorithm and parameters of a behavior can still be adapted online by modifying the data that represents the behavior. Thus, machine learning algorithms can optimize an existing behavior to an unknown situation, e.g., a new environment or a motor failure. With a first implementation, it is shown that the proposed behavior graphs are suited for controlling kinematically complex walking machines.

Keywords :

Robot, Behavior, Control, Machine Learning


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