title: Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.
abstract: It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.
authors.affiliation: Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany jan.kneissler@uni-tuebingen.de.
authors.forename: Jan
authors.initials: J
authors.lastname: Kneissler
authors.forename: Patrick O
authors.initials: PO
authors.lastname: Stalph
authors.forename: Jan
authors.initials: J
authors.lastname: Drugowitsch
authors.forename: Martin V
authors.initials: MV
authors.lastname: Butz
mesh_terms: D001185:Artificial Intelligence / Q000639:trends*; D001696:Biomechanical Phenomena; D008962:Models, Theoretical; D012044:Regression Analysis; D012371:Robotics / Q000379:methods*; D013997:Time Factors