Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization
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Abstract (arXiv)
Conformal Prediction (CP) is a powerful statistical machine learning tool to construct uncertainty sets with coverage guarantees, which has fueled its extensive adoption in generating prediction regions for decision-making tasks, e.g., Trajectory Optimization (TO) in uncertain environments. However, existing methods predominantly employ a sequential scheme, where decisions rely unidirectionally on the prediction regions, and consequently the information from decision-making fails to be fed back to instruct CP. In this paper, we propose a novel Feedback-Based CP (Fb-CP) framework for shrinking-horizon TO with a joint risk constraint over the entire mission time. Specifically, a CP-based posterior risk calculation method is developed by fully leveraging the realized trajectories to adjust the posterior allowable risk, which is then allocated to future times to update prediction regions. In this way, the information in the realized trajectories is continuously fed back to the CP, enabling attractive feedback-based adjustments of the prediction regions and a provable online improvement in trajectory performance. Furthermore, we theoretically prove that such adjustments consistently maintain the coverage guarantees of the prediction regions, thereby ensuring provable safety. Additionally, we develop a decision-focused iterative risk allocation algorithm with theoretical convergence analysis for allocating the posterior allowable risk which closely aligns with Fb-CP. Furthermore, we extend the proposed method to handle distribution shift. The effectiveness and superiority of the proposed method are demonstrated through benchmark experiments.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 18 Oct 2025.
Builds on
32 of 50 references.
References · 32
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- 2019Conformal Prediction Under Covariate Shiftcited by 519
- 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
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- 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 54
20 more
- 2022Adaptive Conformal Prediction for Motion Planning among Dynamic Agentscited by 53
- 2023Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictionscited by 50
- 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 42
- 2024Length Optimization in Conformal Predictioncited by 39
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- 2023Conformal Prediction Regions for Time Series using Linear Complementarity Programmingcited by 33
- 2024Model-Based Diffusion for Trajectory Optimizationcited by 32
- 2022Efficient and Differentiable Conformal Prediction with General Function Classescited by 26
- 2024Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)cited by 26
- 2023Multi-Agent Reachability Calibration with Conformal Predictioncited by 25
- 2023Robots That Can See: Leveraging Human Pose for Trajectory Predictioncited by 23
- 2024Robust Yet Efficient Conformal Prediction Setscited by 16
- 2023Conformal Predictive Safety Filter for RL Controllers in Dynamic Environmentscited by 14
- 2024Adaptive Conformal Inference by Bettingcited by 12
- 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
- 2024Conformalized Adaptive Forecasting of Heterogeneous Trajectoriescited by 10
- 2020Wasserstein Distributionally Robust Motion Control for Collision Avoidance Using Conditional Value-at-Riskcited by 8
- 2024Safety-Critical Control with Uncertainty Quantification using Adaptive Conformal Predictioncited by 7
- 2024Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guaranteescited by 4
- 2024Conformal Off-Policy Prediction for Multi-Agent Systemscited by 3
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Citing papers per year: 2026: 3.
3 papers.