Explainable AI for Interpretable Robot Decision-Making
Abstract
In the context of robotics, decision-making is a crucial aspect of autonomous systems. Ensuring that robots make transparent and interpretable decisions is of paramount importance, particularly in applications where human-robot collaboration, safety, and trust are essential. This paper delves into the realm of explainable AI (XAI) techniques as a means to enhance the interpretability of robot decision-making processes. It explores various methods and approaches, such as rule-based systems, model-agnostic interpretability tools, and explainable machine learning, aimed at making robots' decisions more comprehensible to humans.
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