Signal Temporal Logic Synthesis as Probabilistic Inference

Ki Myung Brian Lee; Chanyeol Yoo; Robert Fitch

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arXiv · 2105.06121

Signal Temporal Logic Synthesis as Probabilistic Inference

Ki Myung Brian Lee·Chanyeol Yoo·Robert Fitch

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Abstract

We reformulate the signal temporal logic (STL) synthesis problem as a maximum a-posteriori (MAP) inference problem. To this end, we introduce the notion of random STL~(RSTL), which extends deterministic STL with random predicates. This new probabilistic extension naturally leads to a synthesis-as-inference approach. The proposed method allows for differentiable, gradient-based synthesis while extending the class of possible uncertain semantics. We demonstrate that the proposed framework scales well with GPU-acceleration, and present realistic applications of uncertain semantics in robotics that involve target tracking and the use of occupancy grids.

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Ki Myung Brian Lee, Chanyeol Yoo, Robert Fitch. 2021-05-13. Signal Temporal Logic Synthesis as Probabilistic Inference. https://arxiv.org/abs/2105.06121

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