From 03.10.10 to 08.10.10,the Dagstuhl Seminar 10401 ``Learning, Planning and Sharing Robot Knowledge for Human-Robot Interaction \u27\u27  was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available

Alami, Rachid

Henderson, Thomas C.

Kirsch, Alexandra

Dagstuhl Research Online Publication Server

10401 Abstracts CollectionLearning, Planning and Sharing RobotKnowledge for Human-Robot Interaction— Dagstuhl Seminar —Rachid Alami1, Rüdiger Dillmann2, Thomas C. Henderson3 and AlexandraKirsch41 LAAS - Toulouse, FRrachid@laas.fr2 Universität Karlsruhe, DEdillmann@kit.edu3 University of Utah, USthenders@nsf.gov4 TU München, DEkirsch@in.tum.deAbstract. From 03.10.10 to 08.10.10, the Dagstuhl Seminar 10401 “Learn-ing, Planning and Sharing Robot Knowledge for Human-Robot Interac-tion ” was held in Schloss Dagstuhl – Leibniz Center for Informatics.During the seminar, several participants presented their current research,and ongoing work and open problems were discussed. Abstracts of thepresentations given during the seminar as well as abstracts of seminar re-sults and ideas are put together in this paper. The first section describesthe seminar topics and goals in general. Links to extended abstracts orfull papers are provided, if available.Keywords. Human-robot interaction, Robot knowledge representationand sharing, Learning, Planning10401 Summary – Learning, Planning and Sharing RobotKnowledge for Human-Robot InteractionAutonomous robots acting in unstructured environments need sophisticated cog-nitive capabilities including perception, manipulation and navigation, planningand reasoning as well as knowledge acquisition and processing skills. Only whenall these capabilities are combined can the robot show intelligent behavior andact appropriately in environments that are primarily designed for humans. Giventhe growing availability and inter-connectivity of modern agents and robots,better mechanisms to define, learn and share knowledge must be developed. Ad-vances in robot platforms have led to their integration into society for a varietyof functions, and there is a pressing need to understand how they can acquireand exploit knowledge required for their specific activities. In order to be useful,Dagstuhl Seminar Proceedings 10401Learning, Planning and Sharing Robot Knowledge for Human-Robot Interactionhttp://drops.dagstuhl.de/opus/volltexte/2011/29332 Rachid Alami, Rüdiger Dillmann, Thomas C. Henderson and AlexandraKirschrobotic agents must be able to recognize a wide range of objects, relations, andsituations in their environment, and to understand the semantics of these. In ad-dition, it is imperative that procedural and process knowledge also be known sothat interactions are safe and meaningful. Cooperation and collaboration are alsoessential as well as the ability to perceive human and animal emotions and inten-tions to the largest degree possible. This seminar brought together 25 scientistswith experience in planning, learning, knowledge processing and human-robotinteraction to discuss how the high-level control concepts are to be used andadapted to human-robot interaction. Because high-level control of robots inter-acting with humans is of special practical importance, we invite experts withexperience in assistive technology and those examining social acceptance of suchsystemsKeywords: Human-robot interaction, Robot knowledge representation andsharing, Learning, PlanningExtended Abstract: http://drops.dagstuhl.de/opus/volltexte/2011/2935Forms of Robot Knowledge AcquisitionThomas C. Henderson (University of Utah, US)Robot knowledge acquisition can be achieved through experience from sensorand actuator data, or by means of logical inference on knowledge structures,or by directly incorporating the knowledge into the robot’s knowledge base.The latter can be done directly by humans (e.g., adding code) or by the robotdownloading knowledge structures directly to its knowledge base. The source ofsuch knowledge may be humans or agents (e.g., from the internet). In addition,robots can share not only knowledge and data, but also their physical platform(e.g., by teleoperation).Keywords: Robot Knowdge Acquisition, Knowledge SharingIntelligent Guidance of an Unmanned HelicopterGordon K. Lee (San Diego State University, US)The objective of this project is to define a science which allows for the evolutionof complex software systems that can fully utilize massively parallel computersof ever-greater capability (initially dual quad cores running fewer higher-levelsensors for tractability) and employ this design in human-robot interaction witha final goal of full autonomy of the robot (in this case, an unmanned helicopter).In order to test a new learning approach that can bestow intelligence on a system,based upon human (tutor) Ű robot (student) interaction, we are working jointlyto design an intelligent system architecture, based on the KASER (KnowledgeAmplification by Structural Expert Randomization), and plan to implement theLearning, Planning and Sharing Robot Knowledge for Human-RobotInteraction 3methodology on an aerial vehicle (a helicopter), which will execute several ma-neuvers such as basic hovering, steep approach, confined area approach, andbasic altitude flying. The software to perform these maneuvers will be developedusing two methods (teach mode with a human-in-the-loop) and classical control.Then, the knowledge amplification system will autonomously generate anotherset of software, which will then be tested and compared to the execution of thebaseline two software codes using the same helicopter flight scenarios. We antici-pate that such complex functional real-time software can be most cost-effectivelywritten through the use of a software-writing system embodying knowledge am-plification.Keywords: Evolution, Heuristics, KASER, System of Systems, Unmanned He-licopterJoint work of: Rubin, Stuart H.; Lee, Gordon K.Full Paper: http://drops.dagstuhl.de/opus/volltexte/2011/2930The Task-State Coordination Pattern, with applications inHuman-Robot-InteractionIngo Luetkebohle (Universität Bielefeld, DE)We consider interaction a powerful enabling technology for robots in humanenvironments. Besides taking commands or reporting, many other uses, suchas interactive learning, are already being explored. However, HRI also posessystems engineering challenges that may hinder its adoption. To address these,we advocate a general coordination pattern for task execution: The Task-StatePattern.Crucially, it separates interaction coordination from task-level control, thusenabling independent, but integrated, development.In the pattern, tasks are represented using both a general, re-usable taskcoordination model and a task-type dependent specification. We have introduceda coordination model rich enough to support a powerful user experience, but stillgeneral enough to accomodate a variety of tasks, thus simplifying architectureand integration. Furthermore, because it is re-used in many places, it providesan attractive target for tool support.Keywords: Coordination, software architecture, design pattern, dialog, human-robot-interactionJoint work of: Luetkebohle, Ingo; Peltason, Julia; Wrede, Britta; Wachsmuth,SvenExtended Abstract: http://drops.dagstuhl.de/opus/volltexte/2011/29314 Rachid Alami, Rüdiger Dillmann, Thomas C. Henderson and AlexandraKirschHuman Models for Human-Robot InteractionKatsu Yamane (Disney Research - Pittsburgh, US)This paper discusses various human models required in human-robot interactionstudy. Good human models are critical for robots to realize effective interactions.For example, human behavior model is necessary to choose appropriate costfunctions to determine robot actions that are comfortable for humans. We willpresent several case studies that attempt to model the human physiology andbehavior, as well as their application to humanoid and character control.Keywords: Human-robot interaction, neuro-muscular human model, learningby demonstrationExtended Abstract: http://drops.dagstuhl.de/opus/volltexte/2011/2932

10401 Abstracts Collection -- Learning, Planning and Sharing Robot Knowledge for Human-Robot Interaction

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10401 Abstracts Collection -- Learning, Planning and Sharing Robot Knowledge for Human-Robot Interaction

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