Evolutionary robotics techniques used to model information and control of visually guided braking

This paper utilizes evolutionary robotics techniques as a hypothesis generator to explore optical variables and control strategies that could be used to solve a driving-like braking task. Given such a task, humans exhibit two different braking behaviors: continuously regulated braking and impulsive braking. Based on an oft-used experimental task in human perception/action research, a series of evolutionary robotics simulations were developed to explore the space of possible braking strategies by examining how braking strategies change as the optical information is manipulated. Our results can be summarized as follows: (1) behaviors similar to human behavior were observed only when the constraints were selected correctly; (2) the optical variables τ and proportional rate yielded significantly better braking performance; (3) two different classes of impulsive braking behaviors were observed, including one not reported in previous studies: discrete impulsive braking and oscillatory impulsive braking; (4) the optical variable τ is used to initiate and terminate braking; (5) the evolved model agents use a proportional rate control strategy to regulate braking continuously. We argue that combining psychological experiments and evolutionary robotics simulations is a promising research methodology that is useful for testing existing hypotheses and generating new ones.