Explainable Artificial Intelligence in Robotics: Building Transparent and Trustworthy Autonomous Systems

Abstract

The change in the field of automation has been brought by the use of Artificial Intelligence (AI) in the robots and this has enabled the autonomous systems to perceive, reason and act in dynamic and complex environments. Nevertheless, the more intelligent and autonomous the robotic systems are, the less transparent their decision-making procedures have become, and that has become a significantly worrisome topic concerning transparency, accountability, and the human trust. Explainable Artificial Intelligence (XAI) in robotics is a solution to such problems that allows AI-based decisions to be understandable and explainable to human operators without affecting the system performance. XAI will enable the human-centered approach by inserting explainability in the structure of robotic intelligence to trace the reasons behind the decision, justify it, and revise it. This interpretability is essential to implement it in healthcare, defense, and industrial robotics where accountability and ethical compliance are the main priorities. Robotics XAI frameworks use a variety of methods, such as model simplification, post-hoc interpretability, saliency mapping and causal reasoning, to explain how and why a robot system makes a specific decision. In addition, the idea of having explainable models that are integrated with multimodal sensor fusion and cognitive reasoning mechanisms improves the user confidence and facilitates cooperation between human beings and robots. The paper expounds on the theoretical basis and the implications of XAI on robots with respect to the trade-off between transparency and performance. It also explores the new approaches to explainability in deep learning-based robotics, which emphasizes the importance of user interpretability, ethical confidence, and real-time feedback. The paper underlines the fact that explainability is not just a technical aspect but a vital element in achieving trust and legal adherence and responsible innovation in the next-generation autonomous robotics.