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Fully Self-Supervised Learning of an Arm Model

Performing a mere infinite number of different movements in our everyday life happens mostly in an automatic and unconscious way, requiring hardly any attention. One necessary requirement for generating different movements dependent on the current circumstances is knowledge about redundant behavioral alternatives and the capability to flexibly choose the current best one. In this paper, we evaluate an architecture that learns to represent such behavioral alternatives in the form of a modular body model from scratch. Moreover, the architecture is able to selectively choose between the behavioral alternatives, yielding kinematic control commands. The proposed architecture combines (temporal) Hebbian learning mechanisms for learning the body model with model-based reinforcement learning techniques for controlling the body. We evaluate the current system capabilities, comparing several configurations and parameter dependencies. Our results show that the architecture can robustly learn a highly flexible arm control system.