$f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception

Understand

While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as self-driving vehicles.

• Robot autonomy stacks also require these otherwise blackbox models to produce reliable and calibrated measures of confidence on their predictions.
• Existing approaches estimate uncertainty from these neural network perception stacks by modifying network architectures, inference procedure, or loss functions.
• However, in general, these methods lack calibration, meaning that the predictive uncertainties do not faithfully represent the true underlying uncertainties (process noise).