Multimodal Uncertainty

Multimodal uncertainty, the presence of multiple possible outcomes in a prediction, is a critical challenge in various fields, particularly robotics and computer vision. Current research focuses on developing methods to accurately represent and reason with these multimodal distributions, employing techniques like non-parametric probability distributions, mixture models (e.g., mixtures of stochastic experts), and novel loss functions (e.g., hinge-Wasserstein) within deep learning frameworks. Addressing multimodal uncertainty is crucial for improving the reliability and safety of autonomous systems, enabling more robust decision-making in complex and unpredictable environments.