DeepAI AI Chat
Log In Sign Up

Teaching robots to perceive time – A reinforcement learning approach (Extended version)

by   Inês Lourenço, et al.

Time perception is the phenomenological experience of time by an individual. In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots to take a step towards temporal cognition. Our framework follows a twofold biologically inspired approach. The first step consists of estimating the passage of time from sensor measurements, since environmental stimuli influence the perception of time. Sensor data is modeled as Gaussian processes that represent the second-order statistics of the natural environment. The estimated elapsed time between two events is computed from the maximum likelihood estimate of the joint distribution of the data collected between them. Moreover, exactly how time is encoded in the brain remains unknown, but there is strong evidence of the involvement of dopaminergic neurons in timing mechanisms. Since their phasic activity has a similar behavior to the reward prediction error of temporal-difference learning models, the latter are used to replicate this behavior. The second step of this approach consists therefore of applying the agent's estimate of the elapsed time in a reinforcement learning problem, where a feature representation called Microstimuli is used. We validate our framework by applying it to an experiment that was originally conducted with mice, and conclude that a robot using this framework is able to reproduce the timing mechanisms of the animal's brain.


page 1

page 2

page 3

page 4


Interval timing in deep reinforcement learning agents

The measurement of time is central to intelligent behavior. We know that...

Reinforcement Learning Framework for Deep Brain Stimulation Study

Malfunctioning neurons in the brain sometimes operate synchronously, rep...

NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)

This workshop explores the interface between cognitive neuroscience and ...

Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments

In this paper, we confront the problem of applying reinforcement learnin...

Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales

Circadian rhythms govern most essential biological processes in the huma...

Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data

In food science, it is of great interest to get information about the te...