Abstract

The rapid advancement of AI-driven robotic systems has introduced significant challenges related to transparency and trust, particularly in safety-critical applications. This review paper critically examines the current approaches to Explainable AI (xAI) in robotics, emphasizing the inherent trade-offs between performance and transparency. While high-performance AI models are essential for complex robotic tasks, their opacity often undermines trust and limits adoption. To address this, the paper proposes a comprehensive framework for implementing xAI in robotics, including strategies such as modular architecture, hybrid models, and human-centered design. The paper also discusses key design considerations and evaluation metrics that ensure a balance between interpretability and operational effectiveness. Finally, the paper reflects on the implications of these strategies for the future of robotics. It suggests avenues for further research to enhance the integration of xAI, aiming to create more trustworthy and reliable robotic systems.