2025
Deep Modeling of Non-Gaussian Aleatoric Uncertainty
RA-L 2025
Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic state estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental dee