Signal Temporal Logic Synthesis as Probabilistic Inference

Lee, Ki Myung Brian; Yoo, Chanyeol

doi:10.1109/icra48506.2021.9560929

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…al. [23] extend STL with uncertain events as predicates to formulate a controller synthesis problem as probabilistic inference, and the authors in [24] model the risk of violating safety specifications over a random variable.…”

Section: A Related Workmentioning

confidence: 99%

Model Predictive Robustness of Signal Temporal Logic Predicates

Lin,

Li,

Althoff

2023

IEEE Robot. Autom. Lett.

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The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. The calculation of robustness is based on evaluating the robustness of underlying predicates. However, the robustness of predicates is usually defined in a model-free way, i.e., without including the system dynamics. Moreover, it is often nontrivial to define the robustness of complicated predicates precisely. To address these issues, we propose a notion of model predictive robustness, which provides a more systematic way of evaluating robustness compared to previous approaches by considering model-based predictions. In particular, we use Gaussian process regression to learn the robustness based on precomputed predictions so that robustness values can be efficiently computed online. We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of precision. By incorporating our robustness definitions into a trajectory planner, autonomous vehicles obey traffic rules more robustly than human drivers in the dataset.Index Terms-Formal methods in robotics and automation, integrated planning and learning, signal temporal logic, model predictive robustness, Gaussian process regression.

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Section: A Related Workmentioning

confidence: 99%

Model Predictive Robustness of Signal Temporal Logic Predicates

Lin,

Li,

Althoff

2023

IEEE Robot. Autom. Lett.

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show abstract

“…al. [22] extend STL with uncertain events as predicates to formulate a controller synthesis problem as probabilistic inference. Nevertheless, these works only evaluate the signal in the form of probability distributions instead of the robustness based on stochastic methods.…”

Section: A Related Workmentioning

confidence: 99%

Model Predictive Robustness of Signal Temporal Logic Predicates

Lin¹,

Li²,

Althoff³

2022

Preprint

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The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. The calculation of robustness is based on evaluating the robustness of underlying predicates. However, the robustness of predicates is usually defined in a model-free way, i.e., without including the system dynamics. Moreover, it is often nontrivial to define the robustness of complicated predicates precisely. To address these issues, we propose a notion of model predictive robustness, which provides a more systematic way of evaluating robustness compared to previous approaches by considering model-based predictions. In particular, we use Gaussian process regression to learn the robustness based on precomputed predictions so that robustness values can be efficiently computed online. We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of expressiveness. By incorporating our robustness definitions into a trajectory planner, autonomous vehicles obey traffic rules more robustly than human drivers in the dataset.

show abstract