Context-dependent predictions and cognitive arm control with XCSF

Abstract

While John Holland has always envisioned learning classifier systems (LCSs) as cognitive systems, most work on LCSs has focused on classification, datamining, and function approximation. In this paper, we show that the XCSF classifier system can be very suitably modified to control a robot system with redundant degrees of freedom, such as a robot arm. Inspired by recent research insights that suggest that sensorimotor codes are nearly ubiquitous in the brain and an essential ingredient for cognition in general, the XCSF system is modified to learn classifiers that encode piecewise linear sensorimotor structures, which are conditioned on prediction-relevant contextual input. In the investigated robot arm problem, we show that XCSF partitions the (contextual) posture space of the arm in such a way that accurate hand movements can be predicted given particular motor commands. Furthermore, we show that the inversion of the sensorimotor predictive structures enables accurate goal-directed closed-loop control of arm reaching movements. Besides the robot arm application, we also investigate performance of the modified XCSF system on a set of artificial functions. All results point out that XCSF is a useful tool to evolve problem space partitions that are maximally effective for the encoding of sensorimotor dependencies. A final discussion elaborates on the relation of the taken approach to actual brain structures and cognitive psychology theories of learning and behavior.