Teaching Robots to Perceive Time: A Twofold Learning Approach

Abstract

The concept of time perception is used to describe the phenomenological experience of time. There is strong evidence that dopaminergic neurons are involved in the timing mechanisms responsible for time perception. The phasic activity of these neurons resembles the behavior of the reward prediction error in temporal-difference learning models. Therefore, these models are used to replicate the neuronal behaviour of the dopamine system and corresponding timing mechanisms. However, time perception has also been shown to be shaped by time estimation mechanisms from external stimuli. In this paper we propose a framework that combines these two principles, in order to provide temporal cognition abilities to intelligent systems such as robots. A time estimator based on observed environmental stimuli is combined with a reinforcement learning approach, using a feature representation called Microstimuli to replicate dopaminergic behaviour. The elapsed time perceived by the robot is estimated by modeling sensor measurements as Gaussian processes to capture the second-order statistics of the natural environment. The proposed framework is evaluated on a simulated robot that performs a temporal discrimination task originally performed by mice. The ability of the robot to replicate the timing mechanisms of the mice is demonstrated by the fact that both exhibit the same ability to classify the duration of intervals.