Myopically Verifiable Probabilistic Certificates for Safe Control and Learning
Zhuoyuan Wang, Haoming Jing, Christian Kurniawan, Albert Chern, Yorie Nakahira

TL;DR
This paper introduces probabilistic invariance techniques for designing safety certificates in stochastic systems, enabling real-time safe control and learning with long-term safety guarantees using short-term outlooks.
Contribution
It proposes a novel probabilistic invariance method that ensures long-term safety through myopic controllers and learning algorithms, balancing safety and computational efficiency.
Findings
Efficient safety assurance using neural networks and MPC with short horizons
Guarantees of long-term safety during and after training
Validated performance in numerical simulations
Abstract
This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit significant long-term risks due to cumulative uncertainties/risks. On the other hand, reachability-based approaches that account for the long-term future may require prohibitive computation in real-time decision making. To overcome this challenge involving stringent long-term safety vs. computation tradeoffs, we first introduce a novel technique termed 'probabilistic invariance'. This technique characterizes the invariance conditions of the probability of interest. When the target probability is defined using long-term trajectories, this technique can be used to design myopic…
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Taxonomy
TopicsFault Detection and Control Systems · Formal Methods in Verification · Bayesian Modeling and Causal Inference
MethodsSparse Evolutionary Training · Focus
