TL;DR
This paper introduces a language-guided adaptive safety framework for autonomous systems that guarantees long-term safety under uncertainty while accommodating human preferences.
Contribution
It presents a novel probabilistic safety certificate framework that integrates natural language inputs and Bayesian estimators for adaptive, personalized safety guarantees.
Findings
Enhanced safety-performance trade-offs demonstrated in simulations.
Framework adapts to changing environments and user preferences.
Code available at https://github.com/hoshino06/adaptive_lane_keeping.
Abstract
Achieving long-term safety in uncertain/extreme environments while accounting for human preferences remains a fundamental challenge for autonomous systems. Existing methods often trade off long-term guarantees for fast real-time control and cannot adapt to variability in human preferences or risk tolerance. To address these limitations, we propose a language-guided adaptive probabilistic safety certificate (PSC) framework that guarantees long-term safety for stochastic systems under environmental uncertainty while accommodating diverse human preferences. The proposed framework integrates natural-language inputs from users and Bayesian estimators of the environment into adaptive safety certificates that explicitly account for user preferences, system dynamics, and quantified uncertainties. Our key technical innovation leverages probabilistic invariance--a generalization of forward…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
