[robotics-worldwide] [meetings] [CFP] IEEE TCDS Special Issue on Adaptive Personal Robot Interaction

Ross Mead (USC) rossmead at usc.edu
Mon Aug 7 01:54:48 PDT 2017

*CFP: IEEE TCDS Special Issue on Adaptive Personal Robot Interaction*

*IEEE Transactions on Cognitive and Developmental Systems*

*Special Issue on Adaptive Personal Robot Interaction*


30 September 2017 – Extended deadline for manuscript submission
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.


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

·        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

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.


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

·        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


Manuscripts should be prepared according to the “Information for Authors”
of the journal found athttp://cis.ieee.org/publications.html. Submissions
should be done through the IEEE TCDS Manuscript center:
https://urldefense.proofpoint.com/v2/url?u=https-3A__mc.manuscriptcentral.com_tcds-2Dieee&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=TPNb0T8BCj_KDZfnsDA_t7Hi58MKlzjHdWQqgj5UqBg&s=pmTpECE3ZowWFKBp4JLMXMTIGqCQ9mu5BRD_ESJTtNA&e=  and please select the category
“SI: Adaptive Human Robot Interaction”.


Dr. Jiqiang SONG
Intel Labs China, Beijing, China
jiqiang.song at intel.com

Dr. Yimin ZHANG
Intel Labs China, Beijing, China
yimin.zhang at intel.com

Prof. Xiaoping CHEN
Department of Computer Science, University of Science and Technology of
China, Hefei, China
xpchen at ustc.edu.cn

Dr. Takayuki KANDA
Intelligent Robotics and Communication Laboratory, ATR (Advanced
Telecommunications Research Institute International), Kyoto, Japan
kanda at atr.jp

- Ross

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