[robotics-worldwide] Deadline extended: IROS 2013 workshop on AI-based Robotics
tenorth at cs.uni-bremen.de
Wed Aug 28 08:00:27 PDT 2013
CALL FOR PAPERS
IROS 2013 Workshop on AI-based Robotics
Thursday, November 7, 2013
IMPORTANT DATES -- Deadline extended!
Extended submission deadline: September 8th, 2013
Notification of acceptance: October 1st, 2013
Submission of final papers: October 15th, 2013
Workshop at Tokyo Big Sight: November 7th, 2013
Motivation and Objectives
Over the past years, research in Artificial Intelligence has made
significant progress in mining knowledge from the web, aggregating
information from different sources and using this knowledge to answer
complex queries. Watson's victory over human jeopardy players
impressively showed that very complex tasks in terms of knowledge
acquisition, question answering, and natural language understanding
can nowadays be solved. Systems like Apple's SIRI or Google now can
understand commands based on the user's location, daily schedule and
So far, such technology has not been applied much in robotics.
Recently, however, we have seen a strong trend towards using semantic
information in robotics, for instance in form of semantic maps, and
believe that having access to AI methods will be crucial for robots
to become intelligent co-workers and skilled household assistants.
In the past year, there have been several efforts from both the AI
and robotics community to bring these research areas closer together:
The AAAI Spring Symposia 2012 and 2013 on "Designing Intelligent
Robots: Re-integrating AI" assembled researchers from the AI community,
the "AI meets Robotics" workshop series provided a forum mainly for
European researchers. With this workshop during the IROS 2013
conference, we would like to make the robotics community aware of the
state of the art in this area, to examine how AI methods 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
* 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.
* Task- and motion planning and execution: Planning and executing
complex tasks requires the combination of symbolic task-level
planning with methods for planning and executing continuous motions.
How can motions be parameterized considering the robot's background
knowledge? How can the task-level executive gain deeper understanding
of the goals and effects of lower-level motions?
* Experience-based learning: Robots will operate over an extended
period of time, repeatedly performing similar tasks. How can they
accumulate semantically annotated experiences, and how can they
extract generalizable knowledge from them? Can approaches like
``big data AI'' be applied to robot experiences to extract useful
information for future tasks?
* Knowledge acquisition: How to acquire the large amounts of knowledge
required to competently act in human environments? Can web resources
be exploited to help robots with knowledge acquisition?
* Semantic Maps: Environment maps that provide robots with semantic
information have become an important research topic in robotics. How
can these maps benefit from AI methods? How can they be extended to
provide deeper information than just positions of objects?
* System-level AI for robotics: Which frameworks, representations and
reasoning techniques can be applied for large-scale applications in
We invite papers of 6 pages in the standard IROS conference format.
Submissions should describe clearly the problems to which AI techniques
are applied, explain the methods that are used, and give an outlook on
challenges that need to be solved in the future.
Besides technical quality and relevance to the workshop topics, the
submissions will be judged by their novelty, their potential to
generate discussion, and their ability to foster collaboration within
Submissions and questions should be directed to iros-ws at robohow.eu
- Moritz Tenorth, Universität Bremen, Germany
- Odest Chadwicke Jenkins, Brown University, USA
- Alessandro Saffiotti, Örebro University, Sweden
- Michael Beetz, Universität Bremen, Germany
Dr. Moritz Tenorth | tenorth at cs.uni-bremen.de
Universität Bremen | Am Fallturm 1
29359 Bremen | Germany
Tel: +49 421 218-64016 | ai.uni-bremen.de/team/moritz_tenorth
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