Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems. We introduce a novel, yet simple method for GP: given a set of training problems, for each problem, compute an optimal plan for each goal atom in some order, perform goal regression on the resulting plans, and lift the corresponding outputs to obtain a set of first-order Condition → Actions rules. The rules collectively constitute a generalised plan that can be executed as is or alternatively be used to prune the planning search space. We formalise and prove the conditions under which our method is guaranteed to learn valid generalised plans and state space pruning axioms for search. Experiments demonstrate significant improvements over state-of-the-art (generalised) planners with respect to the 3 metrics of synthesis cost, planning coverage, and solution quality on various classical and numeric planning domains.

We investigate the synthesis of policies for high-level agent programs expressed in Golog, a language based on situation calculus that incorporates nondeterministic programming constructs. Unlike traditional approaches for program realization that assume full agent control or rely on incremental search, we address scenarios where environmental nondeterminism significantly influences program outcomes. Our synthesis problem involves deriving a policy that successfully realizes a given Golog program while ensuring the satisfaction of a temporal specification, expressed in Linear Temporal Logic on finite traces (LTLf), across all possible environmental behaviors. By leveraging an expressive class of first-order action theories, we construct a finite game arena that encapsulates program executions and tracks the satisfaction of the temporal goal. A game-theoretic approach is employed to derive such a policy. Experimental results demonstrate this approach's feasibility in domains with unbounded objects and non-local effects. This work bridges agent programming and temporal logic synthesis, providing a framework for robust agent behavior in nondeterministic environments.

Plan execution on a mobile robot is inherently error-prone, as the robot needs to act in a physical world which can never be completely controlled by the robot. If an error occurs during execution, the true world state is unknown, as a failure may have unobservable consequences. One approach to deal with such failures is diagnosis, where the true world state is determined by identifying a set of faults based on sensed observations. In this paper, we present a novel approach to explanatory diagnosis, based on the assumption that most failures occur due to some robot hardware failure. We model the robot platform components with state machines and formulate action variants for the robots' actions, modelling different fault modes. We apply diagnosis as planning with a top-k planning approach to determine possible diagnosis candidates and then use active diagnosis to find out which of those candidates is the true diagnosis. Finally, based on the platform model, we recover from the occurred failure such that the robot can continue to operate. We evaluate our approach in a logistics robots scenario by comparing it to having no diagnosis and diagnosis without platform models, showing a significant improvement to both alternatives.

Task planning for mobile robots typically uses an abstract planning domain that ignores the low-level details of the specific robot platform. Therefore, executing a plan on an actual robot often requires additional steps to deal with the specifics of the robot platform. Such a platform can be modeled with timed automata and a set of temporal constraints that need to be satisfied during execution. In this paper, we describe how to transform an abstract plan into a platform-specific action sequence that satisfies all platform constraints. The transformation procedure first transforms the plan into a timed automaton, which is then combined with the platform automata while removing all transitions that violate any constraint. We then apply reachability analysis on the resulting automaton. From any solution trace one can obtain the abstract plan extended by additional platform actions such that all platform constraints are satisfied. We describe the transformation procedure in detail and provide an evaluation in two real-world robotics scenarios.

To solve ever more complex and longer tasks, mobile robots need to generate more elaborate plans and must handle dynamic environments and incomplete knowledge. We address this challenge by integrating two seemingly different approaches — PDDL-based planning for efficient plan generation and Golog for highly expressive behavior specification — in a coherent framework that supports continual planning. The latter allows to interleave plan generation and execution through assertions, which are placeholder actions that are dynamically expanded into conditional sub-plans (using classical planners) once a replanning condition is satisfied. We formalize and implement continual planning in Golog which was so far only supported in PDDL-based systems. This enables combining the execution of generated plans with regular Golog programs and execution monitoring. Experiments on autonomous mobile robots show that the approach supports expressive behavior specification combined with efficient sub-plan generation to handle dynamic environments and incomplete knowledge in a unified way.