Signal Temporal Logic Synthesis as Probabilistic Inference

2021/05/13 by Lee, Ki Myung Brian, Yoo, Chanyeol, Fitch, Robert
#FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2105.06121

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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