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This work introduces efficient symbolic algorithms for quantitative reactivesynthesis. We consider resource-constrained robotic manipulators that need tointeract with a human to achieve a complex task expressed in linear temporallogic. Our framework generates reactive strategies that not only guarantee taskcompletion but also seek cooperation with the human when possible. We model theinteraction as a two-player game and consider regret-minimizing strategies toencourage cooperation. We use symbolic representation of the game to enablescalability. For synthesis, we first introduce value iteration algorithms forsuch games with min-max objectives. Then, we extend our method to theregret-minimizing objectives. Our benchmarks reveal that our symbolic frameworknot only significantly improves computation time (up to an order of magnitude)but also can scale up to much larger instances of manipulation problems with upto 2x number of objects and locations than the state of the art.

Efficient Symbolic Approaches for Quantitative Reactive Synthesis with Finite Tasks