Hyperproperties for Robotics: Planning via HyperLTL
This work addresses planning challenges in robotics for tasks involving interrelations between multiple paths, but it is incremental as it builds on existing formal methods.
The authors tackled the problem of robotic planning for objectives like optimality and robustness, which require reasoning about multiple paths, by extending synthesis methods to hyper-temporal logics and introducing a symbolic approach for HyperLTL specifications, with evaluation on case studies.
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.