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This paper describes a Python toolbox for active perception and controlsynthesis of probabilistic signal temporal logic (PrSTL) formulas of switchedlinear systems with additive Gaussian disturbances and measurement noises. Weimplement a counterexample-guided synthesis strategy that combines BoundedModel Checking, linear programming, and sampling-based motion planningtechniques. We illustrate our approach and the toolbox throughout the paperwith a motion planning example for a vehicle with noisy localization. The codeis available at \url{https://codeocean.com/capsule/0013534/tree}.

idSTLPy: A Python Toolbox for Active Perception and Control