Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception

2025-09-26

AI Summary

Deep learning models for robotic tasks often yield unreliable point estimates and lack robust uncertainty quantification under out-of-distribution or noisy inputs. While existing conformal prediction (CP) methods provide distribution-free validity guarantees, they rely on fixed, context-agnostic nonconformity scores—leading to overly conservative or unsafe prediction intervals. To address this, we propose a learnable, calibration-preserving prediction framework: a lightweight, context-aware neural network dynamically fuses geometric, semantic, and task-specific features to generate adaptive nonconformity scores, enabling scenario-aware calibration under rigorous theoretical guarantees. Evaluated across seven robotic benchmarks, our method improves detection bounding-box compactness by 46–54%, increases path planning success rate to 91.5%, achieves real-time inference at 39 FPS, and enhances energy efficiency by 7.4×—significantly outperforming conventional CP and ensemble-based approaches.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 task-specific features to produce context-aware uncertainty sets. LCP maintains CP's theoretical guarantees while reducing prediction set sizes by 18% in classification, tightening detection intervals by 52%, and improving path planning safety from 72% to 91% success with minimal overhead. Across three robotic tasks on seven benchmarks, LCP consistently outperforms Standard CP and ensemble baselines. In classification on CIFAR-100 and ImageNet, it achieves smaller set sizes (4.7-9.9% reduction) at target coverage. For object detection on COCO, BDD100K, and Cityscapes, it produces 46-54% tighter bounding boxes. In path planning through cluttered environments, it improves success to 91.5% with only 4.5% path inflation, compared to 12.2% for Standard CP. The method is lightweight (approximately 4.8% runtime overhead, 42 KB memory) and supports online adaptation, making it well suited to resource-constrained autonomous systems. Hardware evaluation shows LCP adds less than 1% memory and 15.9% inference overhead, yet sustains 39 FPS on detection tasks while being 7.4 times more energy-efficient than ensembles.

Problem

• Addressing poorly calibrated confidence in robotic deep learning models
• Improving uncertainty quantification under novel or noisy inputs
• Reducing conservativeness of conformal prediction intervals through context-awareness

Innovation

• Learns context-aware nonconformity functions using neural networks
• Reduces prediction set sizes while maintaining coverage guarantees
• Enables lightweight online adaptation with minimal resource overhead