[image 02329] CFP: IEEE TCDS Special Issue on Adaptive Personal Robot Interaction

[Takayuki Kanda]

Image MLの皆様、

IEEE Transactions on Cognitive and Developmental Systemsでの
"Adaptive Personal Robot Interaction"に関する特集号についての投稿の案内で
す。
投稿締め切りは7/30になっております。

ご興味ありましたら、ぜひ投稿をご検討ください。
よろしくお願いいたします。

神田＠ATR

============================================================
IEEE Transactions on Cognitive and Developmental Systems
Special Issue on Adaptive Personal Robot Interaction
http://cis.ieee.org/component/content/article/19-e-newsletter-publications/7
56-cfp-ieee-tcds-special-issue-on-adaptive-personal-robot-interaction.html/

AIM AND SCOPE
Personal robots have become a highlight in both research community and
industry. They are expected to interact with humans at various scenarios,
bring added value, or even to be respected and loved by humans. These
expectations pose serious challenges to the capability of robots to interact
with humans in a natural manner, especially in terms of cognition.
Interactions could span from information-oriented and task-oriented
interaction, to emotional interaction, and even social interaction. Robots
will accomplish daily tasks when interacting with humans, like personal
assistance, child education, or senior care, independently or
collaboratively. It’s not easy to provide a fixed definition and scope to
any of these tasks, or to program them in advance. Moreover, there is a lot
of work to do in service personalization in order to meet the special needs
of a user. Therefore, robots need to learn continuously and adapt/develop
their capabilities based on their long-term interaction with users. Many
research questions arise and wait for answers before this vision becomes a
reality. Below are a few examples:
  What are the special requirements for adaptive personal robot interaction?
  What kind of theory framework is needed for adaptive personal robot
interaction?
  How can personal robots learn from humans continuously and develop/adapt
their social interaction skills?
  How to adapt multi-modal perception to make them more robust when working
over time?
  How to build trust and respect while interacting with humans? What’s the
internal model of personal robot to adapt and evaluate the progress?

All these questions present serious challenges which call for a
cross-disciplinary approach involving perception and cognition system,
developmental robotics, computer vision, speech/NLP, multi-modality HRI
(Human-Robot Interaction), personalization of robot service, innovation
applications, psychology, user experience, and sensors/computing platforms.
In the past, personal robots was not a mainstream application area, so
researchers didn’t frequently communicate and collaborate with each other.
However, given the clear demand of personal robots and the acceleration of
technology development in related areas, such as robotics, computer vision
and machine learning, personal robots can be equipped with more advanced
interaction capabilities. It’s a great time now to make collective efforts
to attack the challenges underlying adaptive personal robot interaction.
Cross-disciplinary collaboration and innovation are deemed to be vital to
the success of adaptive personal robot interaction. In addition, a
systematic approach, targeting at real challenges in personal robot
application, is important. It’s different from the longer-term approach
that was applied in such areas as developmental robotics and emphasizes
learning from scratch. Instead, it’s believed that the leverage of existing
advances in computer perception is important to the achievement of realistic
solutions. Finally, it’s believed that new solutions should consider
efficient computing platforms, so that such solutions can demonstrate good
feasibility in personal robots.

THEMES
This special issue aims to report state-of-the-art approaches and recent
advances in adaptive personal robot interaction with a cross-disciplinary
perspective, including theory foundation, machine learning and knowledge
acquisition, adaptive perception/cognition sub-systems like computer vision.
Topics relevant to this special issue include but are not limited to:
  Theory framework for adaptive personal robot interaction
  Machine learning algorithms for adaptive interaction
  Adaptive learning for social interaction
  Adaptive computer vision
  Adaptive multi-modal perception/cognition, including multi-modal emotion
recognition etc.
  Adaptive psychology-based emotion engine
  Efficient computing platform for adaptive personal robot interaction

SUBMISSION
Manuscripts should be prepared according to the “Information for Authors”
of the journal found at http://cis.ieee.org/publications.html. Submissions
should be done through the IEEE TCDS Manuscript center:
https://mc.manuscriptcentral.com/tcds-ieee and please select the category
“SI: Adaptive Human Robot Interaction”.


IMPORTANT DATES
30 July 2017 - Deadline for manuscript submission
15 Oct 2017 - Notification of authors
15 November 2017 - Deadline for revised manuscripts
15 December 2017 - Final version
For further information, please contact one of the following Guest Editors.

GUEST EDITOR
Dr. Jiqiang SONG, Intel Labs China, Beijing, China jiqiang.song ＠ intel.com
Dr. Yimin ZHANG, Intel Labs China, Beijing, China yimin.zhang ＠ intel.com
Prof. Xiaoping CHEN, Department of Computer Science, University of Science
and Technology of China, Hefei, China xpchen ＠ ustc.edu.cn
Dr. Takayuki KANDA, ATR Intelligent Robotics and Communication Laboratory,
kanda ＠ atr.jp