J. Benton

Planning for human-robot teaming in open worlds

As the number of applications for human-robot teaming continue to rise, there is an increasing need for planning technologies that can guide robots in such teaming scenarios. In this article, we focus on adapting planning technology to Urban Search And Rescue (USAR) with a human-robot team. We start by showing that several aspects of state-of-the-art planning technology, including temporal planning, partial satisfaction planning, and replanning, can be gainfully adapted to this scenario. We then note that human-robot teaming also throws up an additional critical challenge, namely, enabling existing planners, which work under closed-world assumptions, to cope with the open worlds that are characteristic of teaming problems such as USAR. In response, we discuss the notion of conditional goals, and describe how we represent and handle a specific class of them called open world quantified goals. Finally, we describe how the planner, and its open world extensions, are integrated into a robot control architecture, and provide an empirical evaluation over USAR experimental runs to establish the effectiveness of the planning components.

An LP-based heuristic for optimal planning

One of the most successful approaches in automated planning is to use heuristic state-space search. A popular heuristic that is used by a number of state-space planners is based on relaxing the planning task by ignoring the delete effects of the actions. In several planning domains, however, this relaxation produces rather weak estimates to guide search effectively. We present a relaxation using (integer) linear programming that respects delete effects but ignores action ordering, which in a number of problems provides better distance estimates. Moreover, our approach can be used as an admissible heuristic for optimal planning.

Tell me when and why to do it!: run-time planner model updates via natural language instruction

Robots are currently being used in and developed for critical HRI applications such as search and rescue. In these scenarios, humans operating under changeable and high-stress conditions must communicate effectively with autonomous agents, necessitating that such agents be able to respond quickly and effectively to rapidly-changing conditions and expectations. We demonstrate a robot planner that is able to utilize new information, specifically information originating in spoken input produced by human operators. We show that the robot is able to learn the pre- and postconditions of previously-unknown action sequences from natural language constructions, and immediately update (1) its knowledge of the current state of the environment, and (2) its underlying world model, in order to produce new and updated plans that are consistent with this new information. While we demonstrate in detail the robots successful operation with a specific example, we also discuss the dialogue modules inherent scalability, and investigate how well the robot is able to respond to natural language commands from untrained users.

Finding and exploiting goal opportunities in real-time during plan execution

Autonomous robots that operate in real-world domains face multiple challenges that make planning and goal selection difficult. Not only must planning and execution occur in real time, newly acquired knowledge can invalidate previous plans, and goals and their utilities can change during plan execution. However, these events can also provide opportunities, if the architecture is designed to react appropriately. We present here an architecture that integrates the SapaReplan planner with the DIARC robot architecture, allowing the architecture to react dynamically to changes in the robots goal structures.