Francesco Migliarba

Learnable Conformal Prediction for Safe and Efficient Robotics under Perception and Planning Uncertainties

Deep learning models in robotics often output point estimates with poorly calibrated confidences, offering no native mechanism to quantify predictive reliability under novel, noisy, or out-of-distribution inputs. Conformal prediction (CP) addresses this gap by providing distribution-free coverage gu…