Human-Inspired Robotic Forgetting: Filtering to Improve Estimation Accuracy

S.T. Freedman and J.A. Adams (USA)


Robot Design and Architecture; Artificial Intelligence; Robot Sensing and Fusion; Forgetting


Perfect memory and recall provides a mixed blessing. While flawless recollection of episodic data allows for increased reasoning, photographic memory can hinder a robot’s ability to operate in real-time dynamic environments. Human-inspired forgetting methods may enable robotic systems to rid themselves of out-dated, irrelevant, and erroneous data. This paper presents the Act Simple algorithm and an associated experimental analysis. The Act Simple algorithm is a novel approach to improving robotic performance by filtering data available to existing algorithms. The experimental analysis tested the effectiveness of five forgetting algorithms in a WiFi signal strength estimation task. The results suggest that forgetting can improve estimation accuracy while reducing the number of sensor readings required. The simplified version of Act Simple outperformed the other forgetting methods and ap pears to be a flexible and adaptable means of incorporating human-inspired forgetting into robotic systems.

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