Sound and efficient closed-world reasoning for planning
Artificial Intelligence
Casper: Space Exploration through Continuous Planning
IEEE Intelligent Systems
Incremental natural language processing for HRI
Proceedings of the ACM/IEEE international conference on Human-robot interaction
First steps toward natural human-like HRI
Autonomous Robots
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Probabilistic planning via determinization in hindsight
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Computational complexity of planning with temporal goals
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Finding and exploiting goal opportunities in real-time during plan execution
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Robust spoken instruction understanding for HRI
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Robotics and Autonomous Systems
Planning for temporally extended goals
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The frame problem and knowledge-producing actions
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Optimal limited contingency planning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Conceptual Imitation Learning in a Human-Robot Interaction Paradigm
ACM Transactions on Intelligent Systems and Technology (TIST)
Tell me when and why to do it!: run-time planner model updates via natural language instruction
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Handling open knowledge for service robots
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.