Finite State Controllers (FSCs) are an effective way to represent sequential plans compactly. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans that solve a range of planning problems from a given domain. In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers. We show that hierarchical FSCs can represent generalized plans more compactly than individual FSCs. Moreover, our call mechanism makes it possible to generate hierarchical FSCs in a modular fashion, or even to apply recursion. We also introduce a compilation that enables a classical planner to generate hierarchical FSCs that solve challenging generalized planning problems. The compilation takes as input a set of planning problems from a given domain and outputs a single classical planning problem, whose solution corresponds to a hierarchical FSC. 1 Introduction Finite state controllers (FSCs) are a compact and effective representation commonly used in AI; prominent examples include robotics [ Brooks, 1989 ] and video-games [ Buckland, 2004] . In planning, FSCs offer two main benefits: 1) solution compactness [ B ackstr om et al., 2014 ]; and 2) the ability to represent generalized plans that solve a range of similar planning problems. This generalization capacity allows FSCs to represent solutions to arbitrarily large problems, as well as problems with partial observability and non-deterministic actions [ Bonet et al., 2010; Hu and Levesque, 2011; Srivastava et al., 2011; Hu and De Giacomo, 2013 ] .

Finite State Controllers (FSCs) are an effective way to compactly represent sequential plans. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans (plans that solve a range of planning problems from a given domain). In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers. This call mechanism allows hierarchical FSCs to represent generalized plans more compactly than individual FSCs, to compute controllers in a modular fashion or even more, to compute recursive controllers. The paper introduces a classical planning compilation for computing hierarchical FSCs that solve challenging generalized planning tasks. The compilation takes as input a finite set of classical planning problems from a given domain. The output of the compilation is a single classical planning problem whose solution induces: (1) a hierarchical FSC and (2), the corresponding validation of that controller on the input classical planning problems.