# Safety-Aware Preference-Based Learning for Safety-Critical Control

arXiv: https://arxiv.org/abs/2112.08516 · HTML: https://mlsanity.com/paper/2112.08516/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2021.

## Built on by (most cited first, 7 of 7)

- [End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions](https://mlsanity.com/paper/2212.11365/) (2022)
- [A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles](https://mlsanity.com/paper/2311.02099/) (2023)
- [Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles](https://mlsanity.com/paper/2503.15407/) (2025)
- [Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences](https://mlsanity.com/paper/2306.06296/) (2023)
- [Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization](https://mlsanity.com/paper/2503.19349/) (2025)
- [Safe Reinforcement Learning with Preference-based Constraint Inference](https://mlsanity.com/paper/2603.23565/) (2026)
- [Specifying Reward Functions for RL Without Environment Sampling](https://mlsanity.com/paper/2609.15544/) (2026)

## Abstract (arXiv)

Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conservative behavior, and tuning these controllers to find the ideal trade-off between performance and safety typically requires domain expertise or a carefully constructed reward function. This work presents a design paradigm for systematically achieving behaviors that balance performance and robust safety by integrating safety-aware Preference-Based Learning (PBL) with Control Barrier Functions (CBFs). Fusing these concepts -- safety-aware learning and safety-critical control -- gives a robust means to achieve safe behaviors on complex robotic systems in practice. We demonstrate the capability of this design paradigm to achieve safe and performant perception-based autonomous operation of a quadrupedal robot both in simulation and experimentally on hardware.