  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). Our
key insight is that calibration is only achieved by imposing constraints across
multiple examples, such as those in a mini-batch; as opposed to existing
approaches which only impose constraints per-sample, often leading to
overconfident (thus miscalibrated) uncertainty estimates. By enforcing the
distribution of outputs of a neural network to resemble a target distribution
by minimizing an $f$-divergence, we obtain significantly better-calibrated
models compared to prior approaches. Our approach, $f$-Cal, outperforms
existing uncertainty calibration approaches on robot perception tasks such as
object detection and monocular depth estimation over multiple real-world
benchmarks.
