[agents] Deadline Extended: AAAI Fall Symposium on Knowledge, Skill, and Behavior Transfer in Autonomous Robots

[Matteo Leonetti]

[Please distribute - apologies for multiple postings]

Thanks for the interest in the symposium. Due to numerous requests, the paper 
submission deadline has been extended by one week, and is now June 20. Details 
below.



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AAAI 2014 Fall Symposium on

Knowledge, Skill, and Behavior Transfer in Autonomous Robots

Part of AAAI Symposia Series
http://www.cs.utexas.edu/~matteo/aaai14fss-TRob
November 13-15, 2014   Arlington, VA, USA
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IMPORTANT DATES
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Paper submission (EXTENDED): June 20, 2014
Notification of acceptance: July 11, 2014
Camera-ready submission: September 10, 2014
Symposium: November 13-15, 2014

INVITED SPEAKERS
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- Yiannis Demiris, Imperial College London.
- Stefan Schaal, University of Southern California.
- Peter Stone, University of Texas at Austin.
- Andrea Thomaz, Georgia Tech.
- Manuela Veloso, Carnegie Mellon University.

DESCRIPTION
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Autonomous robots have achieved high levels of performance and reliability at 
specific tasks. However, for them to be practical and effective at everyday 
tasks in our homes and offices, they must be able to learn to perform different 
tasks over time, and rapidly adapt to new situations.

Learning each task in isolation is an expensive process, requiring large 
amounts of both time and data. In robotics, this expensive learning process 
also has secondary costs, such as energy usage and joint fatigue. Furthermore, 
as robotic hardware evolves or new robots are acquired, these robots must be 
trained, which is extremely inefficient if performed tabula rasa.

Recent developments in knowledge representation, machine learning, and optimal 
control provide a potential solution to this problem, enabling robots to 
minimize the time and cost of learning new tasks by building upon knowledge 
acquired from other tasks or by other robots. This ability is essential to the 
development of versatile autonomous robots that can perform a wide variety of 
tasks and rapidly learn new abilities.

Various aspects of this problem have been addressed by different communities in 
artificial intelligence and robotics. This symposium will seek to draw together 
researchers from these different communities toward the goal of enabling 
autonomous robots to support a wide variety of tasks, rapidly and robustly 
learn new abilities, adapt quickly to changing contexts, and collaborate 
effectively with other robots and humans.


TOPICS
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We are seeking broad participation from the areas including, but not limited 
to:

- Transfer in Autonomous Robots: inter-task transfer learning, transfer over 
long sequences of tasks, cross-domain transfer learning, long-term autonomy, 
autonomy in dynamic and noisy environments, lifelong learning, knowledge 
representation, transfer between simulated and real robots.

- Multi-Robot Systems: multi-robot knowledge transfer, task switching in 
multi-robot learning, distributed transfer learning, knowledge/skill transfer 
across heterogeneous robots.

- Human-Robot Interaction: human-robot knowledge/skill transfer, transfer in 
mixed human-robot teams, learning by demonstration, imitation learning.

- Cloud Networked Robotics: access to shared knowledge, reasoning, and skills 
in the cloud, cloud-based knowledge/skill transfer, cloud-based distributed 
transfer learning.

- Applications: testbeds and environments, data sets, evaluation methodology.


SUBMISSIONS
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Contributions can be full-length papers (up to 8 pages), or extended 
abstracts, and late breaking results (2-4 pages). Submissions will be peer 
reviewed and evaluated on both their technical merit along with their 
potential to generate discussion and promote collaboration within the 
community.

Authors should submit their contributions electronically in PDF (AAAI format) 
at: https://www.easychair.org/conferences/?conf=ksbt2014


ORGANIZING COMMITTEE
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Matteo Leonetti (chair), University of Texas at Austin
Eric Eaton (co-chair), University of Pennsylvania
Pooyan Fazli (co-chair), Carnegie Mellon University