Foundation Models (FMs) have emerged as a transformative force in artificial intelligence, enhancing robotic systems with advanced perception, decision-making, and human-robot interaction capabilities. However, their integration into robotics raises significant ethical concerns, including issues of bias, transparency, and safety. This paper examines the ethical challenges associated with FMs in robotics and explores strategies to mitigate bias, improve explainability, and establish regulatory standards. By leveraging fairness-aware training, Explainable AI (XAI) frameworks, and compliance with emerging guidelines such as IEEE and the EU AI Act, ethical AI deployment in robotics can be achieved. Future research should focus on refining these approaches to ensure that FM-driven robotic systems operate in a fair, interpretable, and safe manner.