A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Control
This work addresses a bottleneck in symbolic control for robotics and autonomous systems, offering an incremental improvement over existing smooth approximations by ensuring soundness and asymptotic completeness.
The paper tackles the problem of non-smooth and non-convex cost functions in Signal Temporal Logic (STL) for symbolic control, which hinder scalability in optimization. It proposes a novel smooth robustness approximation that is sound and asymptotically complete, enabling faster gradient-based methods while allowing an explicit tradeoff between conservativeness and completeness.
Recent years have seen an increasing use of Signal Temporal Logic (STL) as a formal specification language for symbolic control, due to its expressiveness and closeness to natural language. Furthermore, STL specifications can be encoded as cost functions using STL's robust semantics, transforming the synthesis problem into an optimization problem. Unfortunately, these cost functions are non-smooth and non-convex, and exact solutions using mixed-integer programming do not scale well. Recent work has focused on using smooth approximations of robustness, which enable faster gradient-based methods to find local maxima, at the expense of soundness and/or completeness. We propose a novel robustness approximation that is smooth everywhere, sound, and asymptotically complete. Our approach combines the benefits of existing approximations, while enabling an explicit tradeoff between conservativeness and completeness.