Towards Lifelong Learning of Optimal Control for Kinematically Complex Robots
In ICRA14 Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots, (ICRA-2014), 31.5.-07.6.2014, Hong Kong, IEEE, Jun/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.