Perception Failure

Perception failure, the inability of autonomous systems to accurately interpret their environment, is a critical challenge hindering the development of safe and reliable robots and autonomous vehicles. Current research focuses on developing robust methods for detecting, mitigating, and even proactively preventing these failures, employing techniques such as reinforcement learning, Q-networks, and causal inference to improve system resilience. This work is crucial for enhancing the safety and reliability of autonomous systems across various applications, from self-driving cars to legged robots navigating complex terrains. The ultimate goal is to create systems that can not only react to perception failures but also anticipate and prevent them, leading to more dependable and trustworthy autonomous technologies.