Effective deployment of multi-robot teams requires solving several interdependent problems
at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must …

The majority of search-based HTN planning systems can be divided into those searching a
space of partial plans (a plan space) and those performing progression search, ie, that build …

Complex, multi-task missions require the coordination of heterogeneous robots at multiple
inter-connected levels, such as coalition formation, scheduling, and motion planning. This …

Temporal planning offers numerous advantages when based on an expressive
representation. Timelines have been known to provide the required expressiveness but at …

There are several formalizations for hierarchical planning. Many of them allow to specify
preconditions and effects for compound tasks. They can be used, eg, to assist during the …

To realize effective heterogeneous multi-robot teams, researchers must leverage individual
robots' relative strengths and coordinate their individual behaviors. Specifically …

This paper presents a planning and execution architecture suited for the initial planning, the
execution and the on-board repair of a plan for a multi-robot mission. The team as a whole …

pyHiPOP is a hierarchical partial-order planner, aimed at solving Hierarchical Task Network
(HTN) planning problems. The current planner version is a re-coding of a version originally …

Industrial robots are required to recover from temporary errors and continue operations
under a changing environment. In this paper, we propose a recovery planning system that …

Navigating multi-agent reinforcement learning (MARL) environments with sparse rewards is
notoriously difficult, particularly in suboptimal settings where exploration can be prematurely …