Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception
Learnable Conformal Prediction (LCP) replaces fixed uncertainty scores with a neural function sθ(x) that adapts to prediction errors using geometric and semantic cues. This allows LCP to produce context-aware uncertainty estimates for robotics, improving coverage guarantees while making predictions tighter in simple cases and wider in difficult ones.