Robot Decision

Robot decision-making research focuses on enabling robots to make safe, efficient, and ethically sound choices in complex, dynamic environments. Current efforts concentrate on improving the reliability and explainability of robot decisions, often employing neural networks (like MLPs), large language models (LLMs), and reinforcement learning algorithms to achieve this. These advancements are crucial for building trustworthy robots capable of interacting safely and effectively with humans, leading to improved performance in applications ranging from assistive robotics to autonomous navigation. A key challenge is addressing bias and ensuring fairness in robot decision-making processes.