Perceptual Uncertainty

Perceptual uncertainty, the inherent unreliability in how we sense and interpret the world, is a central challenge in artificial intelligence and robotics. Current research focuses on developing models and algorithms, such as deep ensembles and diffusion-based methods, that not only make predictions but also quantify their uncertainty, often using information-theoretic frameworks or Bayesian approaches. This improved understanding of uncertainty is crucial for building safer and more robust autonomous systems, particularly in applications like autonomous driving and robotic manipulation, where reliable perception is paramount for decision-making. The ability to accurately represent and manage perceptual uncertainty is driving advancements across various fields, from computer vision to cognitive science.