# Certainty Equivalent Perception-Based Control

arXiv: https://arxiv.org/abs/2008.12332 · HTML: https://mlsanity.com/paper/2008.12332/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation](https://mlsanity.com/paper/2305.16985/) (2023)
- [Online Control of Unknown Time-Varying Dynamical Systems](https://mlsanity.com/paper/2202.07890/) (2022)
- [Smoothed Online Learning for Prediction in Piecewise Affine Systems](https://mlsanity.com/paper/2301.11187/) (2023)
- [Learning Linear Dynamics from Bilinear Observations](https://mlsanity.com/paper/2409.16499/) (2024)
- [Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making](https://mlsanity.com/paper/2302.05430/) (2023)
- [System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models](https://mlsanity.com/paper/2312.06810/) (2023)
- [A computationally lightweight safe learning algorithm](https://mlsanity.com/paper/2309.03672/) (2023)
- [VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis](https://mlsanity.com/paper/2604.24894/) (2026)
- [Decentralized diffusion-based learning under non-parametric limited prior knowledge](https://mlsanity.com/paper/2305.03295/) (2023)
- [Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks](https://mlsanity.com/paper/2212.14115/) (2022)
- [Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers](https://mlsanity.com/paper/2509.05201/) (2025)

## Abstract (arXiv)

In order to certify performance and safety, feedback control requires precise characterization of sensor errors. In this paper, we provide guarantees on such feedback systems when sensors are characterized by solving a supervised learning problem. We show a uniform error bound on nonparametric kernel regression under a dynamically-achievable dense sampling scheme. This allows for a finite-time convergence rate on the sub-optimality of using the regressor in closed-loop for waypoint tracking. We demonstrate our results in simulation with simplified unmanned aerial vehicle and autonomous driving examples.