[robotics-worldwide] [journals] CFP- ACM-THRI Special Issue on Representation Learning for Human and Robot Cognition

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[robotics-worldwide] [journals] CFP- ACM-THRI Special Issue on Representation Learning for Human and Robot Cognition

Takato Horii
ACM Transactions on Human-Robot Interaction (T-HRI)

CALL FOR PAPERS

**Apologies for cross posting **

We are happy to call for papers for the journal special issue:
"Representation Learning for Human and Robot Cognition"

Webpage: https://urldefense.proofpoint.com/v2/url?u=https-3A__thri.acm.org_CFP-2DRLHRC.cfm&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=KmroZKwDBhalwlPZ2TWOY-_D6-2rWPWsyD2woCsrJSA&s=CdP1vO_orKEFIixcDyeMaKBL_2NmcGdaEP8CxoerWN8&e=

I. Aim and Scope

Intelligent robots are rapidly moving to the center of human environment;
they collaborate with human users in different applications that require
high-level cognitive functions so as to allow them to understand and learn
from human behavior within different Human-Robot Interaction (HRI)
contexts. To this end, a stubborn challenge that attracts much attention in
artificial intelligence is representation learning, which refers to
learning representations of data so as to efficiently extract relevant
features for probabilistic, nonprobabilistic, or connectionist classifiers.
This active area of research spans different fields and applications
including speech recognition, object recognition, emotion recognition,
natural language processing, language emergence and development, in
addition to mirroring different human cognitive processes through
appropriate computational modeling.

Learning constitutes a basic operation in the human cognitive system and
developmental process, where perceptual information enhances the ability of
the sensory system to respond to external stimuli through interaction with
the environment. This learning process depends on the optimality of
features (representations of data), which allows humans to make sense of
everything they feel, hear, touch, and see in the environment. Using
intelligent robots could open the door to shed light on the underlying
mechanisms of representation learning and its associated cognitive
processes so as to take a closer step towards making robots able to better
collaborate with human users in space.

This special issue aims to shed light on cutting edge lines of
interdisciplinary research in artificial intelligence, cognitive science,
neuroscience, cognitive robotics, and human-robot interaction, focusing on
representation learning with the objective of creating natural and
intelligent interaction between humans and robots. Recent advances and
future research lines in representation learning will be discussed in
detail in this journal special issue.

II. Potential Topics
Topics relevant to this special issue include, but are not limited to:

      • Language learning, embodiment, and social intelligence
      • Human symbol system and symbol emergence in robotics
      • Computational modeling for high-level human cognitive functions
      • Predictive learning from sensorimotor information
      • Multimodal interaction and concept formulation
      • Language and action development
      • Learning, reasoning, and adaptation in collaborative human-robot
tasks
      • Affordance learning
      • Cross-situational learning
      • Learning by demonstration and imitation
      • Language and grammar induction in robots

III. Submission
ACM Transactions on Human-Robot Interaction is a peer-reviewed,
interdisciplinary, open-access journal using an online submission and
manuscript tracking system. To submit your paper, please:

      • Go to https://urldefense.proofpoint.com/v2/url?u=https-3A__mc.manuscriptcentral.com_thri&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=KmroZKwDBhalwlPZ2TWOY-_D6-2rWPWsyD2woCsrJSA&s=IA-LzhFwXfwGKiApFW8ULlIxnfUNGcdNmKigRV2zxEg&e= and login or follow the
"Create an account" link to register.
      • After logging in, click the "Author" tab.
      • Follow the instructions to "Start New Submission".
      • Choose the submission category  “SI: Representation Learning for
Human and Robot Cognition”.

IV. Timline

      • Deadline for paper submission: July 1, 2018
      • First notification for authors: September 15, 2018
      • Deadline for revised papers submission: November 15, 2018
      • Final notification for authors: January 15, 2019
      • Deadline for submission of camera-ready manuscripts: March 1, 2019
      • Expected publication date: May 2019

V. Guest editors

Takato Horii, The University of Electro-Communications, Japan (
[hidden email]).
Dr. Amir Aly, Ritsumeikan University, Japan ([hidden email]).
Dr. Yukie Nagai, National Institute of Information and Communications
Technology (NICT), Japan ([hidden email]).
Prof. Takayuki Nagai, The University of Electro-Communications, Japan (
[hidden email]).

--
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Takato Horii,
Project Assistant Professor,
Dept. of Mechanical Engineering and Intelligent Systems,
Graduate School of Informatics and Engineering,
The University of Electro-Communications
Intelligent Systems Laboratory (Nagai Lab)
Web: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.er.ams.eng.osaka-2Du.ac.jp_takato_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=KmroZKwDBhalwlPZ2TWOY-_D6-2rWPWsyD2woCsrJSA&s=CrIWbbnWIObW0NAftq49L_BMHhVWMjPJrza1jSXAEm0&e=
Email: [hidden email]
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