Hyperproperties for Robotics: Planning via HyperLTL

TL;DR

This paper extends robotic planning methods to hyper-temporal logics using HyperLTL, enabling the expression of complex objectives like optimality and privacy involving multiple paths, and introduces a symbolic synthesis approach.

Contribution

It introduces HyperLTL for expressing hyperproperties in robotic planning and develops a symbolic synthesis method for such specifications.

Findings

Successfully expressed hyperproperties like privacy and robustness with HyperLTL.

Developed a symbolic planning strategy synthesis approach.

Validated the method on multiple case studies.

Abstract

There is a growing interest on formal methods-based robotic planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-temporal logics. We are motivated by the fact that important planning objectives, such as optimality, robustness, and privacy, (maybe implicitly) involve the interrelation between multiple paths. Such objectives are thus hyperproperties, and cannot be expressed with usual temporal logics like the linear temporal logic (LTL). We show that such hyperproperties can be expressed by HyperLTL, an extension of LTL to multiple paths. To handle the complexity of planning with HyperLTL specifications, we introduce a symbolic approach for synthesizing planning strategies on discrete transition systems. Our planning method is evaluated on several case studies.