[robotics-worldwide] CFP: IROS 2011 Workshop on Knowledge Representation for Autonomous Robots

Moritz Tenorth tenorth at cs.tum.edu
Thu Apr 21 04:00:53 PDT 2011


                           CALL FOR PAPERS

  IROS 2011 Workshop on Knowledge Representation for Autonomous Robots
                       Sunday, September 25, 2011


Paper submission deadline:     25 May  2011
Notification of acceptance:    20 June 2011
Submission of final papers:    30 June 2011

Motivation and Objectives:

Over the past years, computers have made significant progress in mining
knowledge from the web, aggregating information from different sources
and using this knowledge to answer complex queries. Watson's recent
victory over human jeopardy players impressively shows that very complex
tasks in terms of knowledge acquisition, question answering, and natural
language understanding can be solved nowadays.

So far, this technology has not been applied much in the robotics 
domain. We do, however, see a strong trend towards using semantic 
information, for instance in form of semantic maps, and believe that 
semantics will become more and more important. Robots will soon have to 
perform household tasks like cleaning up, setting a table or cooking 
simple meals. These tasks are extremely knowledge-intensive: To 
competently perform them, a robot needs a large amount of knowledge 
about properties of objects, actions required for a task, or execution 
problems that can arise.

In this workshop, we invite researchers from both robotics and knowledge
representation to discuss the state of the art in robot knowledge 
processing, examine how formally represented knowledge can help robots 
in performing their tasks, and identify major research challenges that 
need to be addressed.

List of Topics

* Knowledge representation for robots: Which kinds of knowledge are
   required? Which representation formalisms are suitable for being used
   on autonomous robots? Which aspects (e.g. spatio-temporal reasoning,
   changes in objects and the environment over time) need to be modeled,
   and how can they be expressed in the chosen formalism?

* Grounding and anchoring: Practical methods for grounding abstract
   symbols in percepts and actions, including the selection of the right
   object to be used for a task among multiple alternatives.

* Hybrid Reasoning: AI methods typically focus on discrete, symbolic
   knowledge, but a robot also needs to reason about continuous,
   non-symbolic entities like time, geometry, and resources.  How should
   the two types of reasoning be combined?  Examples include hybrid task
   and motion planning, and combined planning and scheduling.

* Knowledge acquisition: How to acquire the large amounts of knowledge
   required to competently act in human environments? Can web resources
   help robots with knowledge acquisition?

* Knowledge exchange between robots: Knowledge acquisition and learning
   are complex and time-consuming tasks -- can robots profit from sharing
   knowledge? Which kinds of knowledge can be exchanged, and how do they
   have to be processed to be used by a different robot?

* Human-robot interaction: How can knowledge help robots to communicate
   with humans and understand dialogs? Which methods are required for
   disambiguating dialog situations and grounding words in actions and
   perceived objects?

* Knowledge and perception: How can robots relate percepts to their
   background knowledge, and how can they use knowledge to improve

* Living with inconsistent knowledge: A robot's knowledge about the
   environment may become wrong or inconsistent due to sensor errors,
   outdated information or inappropriate knowledge exchange. How can
   the knowledge base handle this situation and still derive useful
   results from the remaining knowledge? Can these inconsistencies be
   detected and resolved?

Invited talks:

Rachid Alami, LAAS-CNRS
Michael Beetz, TU Munich
Chad Jenkins, Brown University
Charlie Kemp, Georgia Tech
Gerhard Lakemeyer, RWTH Aachen
Bhaskara Marthi, Willow Garage
Alessandro Saffiotti, Orebro University


We invite papers of 4-6 pages in the standard IROS format. Submissions
should describe clearly the problems to which knowledge processing
techniques are applied, explain the methods that are being used, and
give an outlook on challenges that need to be solved in the future.

Besides technical quality, the submissions will be judged by their
novelty, their potential to generate discussion, and their ability
to foster collaboration within the community.

Questions should be directed to Moritz Tenorth, tenorth-at-cs.tum.edu

Organizing Committee

Michael Beetz, TU Munich
Rachid Alami, LAAS-CNRS, Toulouse
Joachim Hertzberg, Universitaet Osnabrueck
Alessandro Saffiotti, Orebro University
Moritz Tenorth, TU Munich

Dipl.-Ing. Moritz Tenorth      | tenorth at cs.tum.edu
Technische Universität München | Karlstr. 45
80333 München                  | Germany
ias.cs.tum.edu/people/tenorth  | Tel: +49 89 289 26912

More information about the robotics-worldwide mailing list