Patrick Bechon

Bio: Patrick Bechon is an academic researcher from University of Toulouse. The author has contributed to research in topics: Partial-order planning & Hierarchical task network. The author has an hindex of 3, co-authored 4 publications receiving 29 citations.

HiPOP: Hierarchical Partial-Order Planning.

Abstract: This paper describes a new planner, HiPOP (Hierarchical Partial-Order Planner), which is domain-configurable and uses POP techniques to create hierarchical time-flexible plans. HiPOP takes as inputs a description of a domain, a problem, and some optional userdefined search-control knowledge. This additional knowledge takes the form of a set of abstract actions with optional methods to achieve them. HiPOP uses this knowledge to enrich the output by providing a hierarchical time-flexible partial-order plan that follows the given methods. We show in this paper how to use this additional knowledge in a POP algorithm and provide results on a domain with a strong hierarchy of actions. We compare our approach with other temporal planners on this

Hybrid planning and distributed iterative repair for multi-robot missions with communication losses

Abstract: 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 must accomplish its mission while dealing with online events such as robots breaking down, new objectives for the team, late actions and intermittent communications. We have chosen a “plan then repair” approach where an initial plan is computed offline and updated online whenever disruptive events happen. We have defined an hybrid planner that mixes Partial Order Planning (POP) with a Hierarchical Task Network (HTN)-based modelling of actions. This planner, called HiPOP for Hierarchical Partial-Order Planner, computes plans with temporal flexibility (thus easing its execution) and abstract actions (thus easing the repair process). It uses a symbolic representation of the world and has been extended with geometrical reasoning to adapt to multi-robots missions. Plans are executed in a distributed way: each robot is responsible of executing its own actions, and to propagate delays in its local plan, taking benefit from the temporal flexibility of the plan. When an inconsistency or a failure arises, a distributed repair algorithm based on HiPOP is used to repair the plan, by iteratively removing actions in the plan in order to amend the global plan. This repair is done onboard one of the robot of the team, and takes care of partial communication. The whole architecture has been evaluated through several benchmarks, statistical simulations, and field experiments involving 8 robots.

Using hybrid planning for plan reparation

Abstract: In this work we propose a plan repair algorithm designed to be used in a real-life setting for a team of autonomous robots. This algorithm is built on top of a hybrid planner. This planner mixes partial order planning and hierarchical planning. This allows the creation of a plan with temporal flexibility while using human knowledge to improve the search process. Simulation shows that repairing increases the number of solved problems or at least reduces the number of plans explored. The algorithm uses the same hierarchical knowledge as the underlying planner, thus needing no more human modelling to properly run. We show that using this knowledge can help the reparation, even when some half-executed abstract actions are present in the plan.

Integrating Planning and Execution for a Team of Heterogeneous Robots with Time and Communication Constraints

Abstract: Field multi-robot missions face numerous unavoidable disturbances, such as delays in executing tasks and intermittent communications. Coping with such disturbances requires to endow the robots with high-level decision skills. We present a distributed decision architecture based first on a hybrid planner that can manage decentralized repairs with partial communication, and secondly on a distributed execution algorithm that efficiently propagates delays. This architecture has been successfully experimented on the field for the achievement of surveillance missions involving eight (8) real autonomous aerial and ground robots.

More than a name? on implications of preconditions and effects of compound HTN planning tasks

Abstract: There are several formalizations for hierarchical planning. Many of them allow to specify preconditions and effects for compound tasks. They can be used, e.g., to assist during the modeling process by ensuring that the decomposition methods' plans "implement" the compound tasks' intended meaning. This is done based on so-called legality criteria that relate these preconditions and effects to the method's plans and pose further restrictions. Despite the variety of expressive hierarchical planning formalisms, most theoretical investigations are only known for standard HTN planning, where compound tasks are just names, i.e., no preconditions or effects can be specified. Thus, up to know, a direct comparison to other hierarchical planning formalisms is hardly possible and fundamental theoretical properties are yet unknown. To enable a better comparison between such formalisms (in particular with respect to their computational expressivity), we first provide a survey on the different legality criteria known from the literature. Then, we investigate the theoretical impact of these criteria for two fundamental problems to planning: plan verification and plan existence. We prove that the plan verification problem is at most NP-complete, while the plan existence problem is in the general case both semi-decidable and undecidable, independent of the demanded criteria. Finally, we discuss our theoretical findings and practical implications.

HTN Planning as Heuristic Progression Search

Abstract: 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, i.e., that build the solution in a forward manner. So far, all HTN planners that guide the search by using heuristic functions are based on plan space search. Those systems represent the set of search nodes more effectively by maintaining a partial ordering between tasks, but they have only limited information about the current state during search. In this article, we propose the use of progression search as basis for heuristic HTN planning systems. Such systems can calculate their heuristics incorporating the current state, because it is tracked during search. Our contribution is the following: We introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete. We show that defining systematicity is problematic for search in HTN planning, propose a definition, and show that it is fulfilled by one of our algorithms. Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning. It relaxes the HTN planning model to a classical model that is only used for calculating heuristics. It is updated during search and used to create heuristic values that are used to guide the HTN search. We show that it can be used to create HTN heuristics with interesting theoretical properties like safety, goal-awareness, and admissibility. Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning.

Temporal and Hierarchical Models for Planning and Acting in Robotics

Abstract: The field of AI planning has seen rapid progress over the last decade and planners are now able to find plan with hundreds of actions in a matter of seconds. Despite those important progresses, robotic systems still tend to have a reactive architecture with very little deliberation on the course of the plan they might follow. In this thesis, we argue that a successful integration with a robotic system requires the planner to have capacities for both temporal and hierarchical reasoning. The former is indeed a universal resource central in many robot activities while the latter is a critical component for the integration of reasoning capabilities at different abstraction levels, typically starting with a high level view of an activity that is iteratively refined down to motion primitives. As a first step to carry out this vision, we present a model for temporal planning unifying the generative and hierarchical approaches. At the center of the model are temporal action templates, similar to those of PDDL complemented with a specification of the initial state as well as the expected evolution of the environment over time. In addition, our model allows for the specification of hierarchical knowledge possibly with a partial coverage. Consequently, our model generalizes the existing generative and HTN approaches together with an explicit time representation. In the second chapter, we introduce a planning procedure suitable for our planning model. In order to support hierarchical features, we extend the existing Partial-Order Causal Link approach used in many constraintbased planners, with the notions of task and decomposition. We implement it in FAPE (Flexible Acting and Planning Environment) together with automated problem analysis techniques used for search guidance. We show FAPE to have performance similar to state of the art temporal planners when used in a generative setting. The addition of hierarchical information leads to further performance gain and allows us to outperform traditional planners. In the third chapter, we study the usual methods used to reason on temporal uncertainty while planning. We relax the usual assumption of total observability and instead provide techniques to reason on the observations needed to maintain a plan dispatchable. We show how such needed observations can be detected at planning time and incrementally dealt with by considering the appropriate sensing actions. In a final chapter, we discuss the place of the proposed planning system as a central component for the control of a robotic actor. We demonstrate how the explicit time representation facilitates plan monitoring and action dispatching when dealing with contingent events that require observation. We take advantage of the constraint-based and hierarchical representation to facilitate both plan-repair procedures as well opportunistic plan refinement at acting time.

Hybrid planning and distributed iterative repair for multi-robot missions with communication losses

Abstract: 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 must accomplish its mission while dealing with online events such as robots breaking down, new objectives for the team, late actions and intermittent communications. We have chosen a “plan then repair” approach where an initial plan is computed offline and updated online whenever disruptive events happen. We have defined an hybrid planner that mixes Partial Order Planning (POP) with a Hierarchical Task Network (HTN)-based modelling of actions. This planner, called HiPOP for Hierarchical Partial-Order Planner, computes plans with temporal flexibility (thus easing its execution) and abstract actions (thus easing the repair process). It uses a symbolic representation of the world and has been extended with geometrical reasoning to adapt to multi-robots missions. Plans are executed in a distributed way: each robot is responsible of executing its own actions, and to propagate delays in its local plan, taking benefit from the temporal flexibility of the plan. When an inconsistency or a failure arises, a distributed repair algorithm based on HiPOP is used to repair the plan, by iteratively removing actions in the plan in order to amend the global plan. This repair is done onboard one of the robot of the team, and takes care of partial communication. The whole architecture has been evaluated through several benchmarks, statistical simulations, and field experiments involving 8 robots.

A Hierarchical Planner based on Set-theoretic Models: Towards Automating the Automation for Autonomous Systems.

Abstract: The complexity of today’s autonomous systems renders the manual engineering of control strategies or behaviors for all possible system states infeasible. Therefore, planning algorithms are required that match the capabilities of the system to the tasks at hand. Solutions to typical problems with robotic systems combine aspects of symbolic action planning with sub-symbolic motion planning and control. The problem complexity of this combination currently prohibits online planning without task specific, manually defined heuristics. To counter that we use a set-theoretic approach to model declarative and procedural knowledge which allows for flexible hierarchies of planning tasks. The coordination of the planning tasks on different levels, the classification of information and various views on data are the core functions of hierarchical planning. We propose suitable graph structures to capture all relevant information and discuss the elements of our hierarchical planning algorithm in this paper. Furthermore, we present two use-cases of an autonomous manufacturing system to highlight the capabilities of our system.