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

Abstract

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 guarantees, yet its reliance on fixed nonconformity scores ignores context and can yield intervals that are overly conservative or unsafe. We address this with Learnable Conformal Prediction (LCP), which replaces fixed scores with a lightweight neural function that leverages geometric, semantic, and model cues. Trained to balance coverage, efficiency, and calibration, LCP preserves CP's finite-sample guarantees while producing intervals that adapt to instance difficulty, achieving context-aware uncertainty without ensembles or repeated inference. On the MRPB benchmark, LCP raises navigation success to 91.5% versus 87.8% for Standard CP, while limiting path inflation to 4.5% compared to 12.2%. For object detection on COCO, BDD100K, and Cityscapes, it reduces mean interval width by 46-54% at 90% coverage, and on classification tasks (CIFAR-100, HAM10000, ImageNet) it shrinks prediction sets by 4.7-9.9%. The method achieves real-time performance on resource-constrained edge hardware (Intel NUC, <30W) while simultaneously providing uncertainty estimates along with the mean prediction.