Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals

arXiv:2609.23280 · cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.RO

Submitted: 2026-09-20

Updated: 2026-09-20

Terminology

• Score Function — 23× in this paper · explained in 4 episodes
• Conformal Prediction — 15× in this paper · explained in 5 episodes
• Adaptive Conformal Prediction — 8× in this paper
• Uncertainty Quantification — 6× in this paper · explained in 8 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Stochastic Dynamics — 4× in this paper · explained in 2 episodes
• Conformalized Quantile Regression — 3× in this paper
• Exchangeability — 3× in this paper · explained in 2 episodes
• American Control Conference — 2× in this paper
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Linear Quadratic Regulator — 1× in this paper
• Model Predictive Control (MPC) — 1× in this paper · explained in 5 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Certifiable Safe Model-Based Reinforcement Learning with Control-Affine Dynamics Approximation
• Theoretical Foundations of Conformal Prediction

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving