Towards Lifelong Learning of Optimal Control for Kinematically Complex Robots

Alexander Dettmann, Malte Langosz, Kai von Szadkowski, Sebastian Bartsch

In: ICRA14 Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots. IEEE International Conference on Robotics and Automation (ICRA-2014) May 31-June 7 Hong Kong China IEEE 6/2014.


Robots intended to perform mobile manipulation in complex environments are commonly equipped with an extensive set of sensors and motors, creating a wide range of perception and interaction capabilities. However, to exploit all theoretically possible abilities of such systems, a control strategy is required that allows to determine and apply the best solution for a given task within an appropriate time frame. In this paper, a lifelong self-improving control scheme for kinematically complex robots is presented, which uses simulation-based behavior generation and optimization procedures to create a library of well-performing solutions for varying tasks and conditions, and combines it with case-based selection, evaluation, and online adaptation methods.


Weitere Links

140331_Towards_Lifelong_Learning_of_Optimal_Control_for_Kinematically_Complex_Robots_ICRA_Dettmann.pdf (pdf, 1 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz