Autonomous robots cohabiting with humans will have to achieve recurring tasks while adapting to the changing conditions of the world. A spatio-temporal memory categorizes the experiences of a robot to improve its ability to adapt to its environment. In this paper, we present a spatio-temporal (ST) memory model consisting of a cascade of two adaptive resonance theory (ART) networks: one to categorize spatial events and the other to extract temporal episodes from the robot’s experiences. Artificial emotions are used to dynamically modulate learning and recall of the ART networks based on how the robot is able to carry its task, using a simple model of artificial emotions. Once an episode is recalled, future events can be predicted and used to influence the intentions of the robot. Evaluation of our ST model is done using an autonomous robotic platform that has to deliver objects to people within an office area. Results demonstrate that our model can memorize and recall the experiences of a robot, and that emotional episodes are recalled more often, allowing the robot to use its memory of past experiences early on when repeating a task.
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The authors want to thank the volunteers who participated in the trials. This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Leconte, F., Ferland, F. & Michaud, F. Design and integration of a spatio-temporal memory with emotional influences to categorize and recall the experiences of an autonomous mobile robot. Auton Robot 40, 831–848 (2016) doi:10.1007/s10514-015-9496-2
- Spatio-temporal memory
- Adaptive resonance theory
- Artificial emotions
- Autonomous robots