[robotics-worldwide] [meetings] CfP: IROS 2017 Workshop on Synergies Between Learning and Interaction

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[robotics-worldwide] [meetings] CfP: IROS 2017 Workshop on Synergies Between Learning and Interaction

Baris Akgun
CALL FOR CONTRIBUTIONS AND PARTICIPATION
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Synergies Between Learning and Interaction
Full-Day Workshop at IROS 2017
Vancouver, B.C., Canada
September 28, 2017
Workshop Website: https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_view_iros17sbli&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=MsbfGGWbKyXzbRoo9EdHk0Nd2MGgbiZK8cxgX4qV2Fk&s=NticjScm15_ODPyqIOTRz5QFhXzdAMIhLKGpW2MXviY&e= 
Submission Website: https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Diros17sbli&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=MsbfGGWbKyXzbRoo9EdHk0Nd2MGgbiZK8cxgX4qV2Fk&s=c_vhW9mD2vrClyJVCgd3TGj4H_95DY0m1nzHV-hRngU&e= 
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=== Important Dates ===
Extended Deadline:     August 8, 2017
Notification of acceptance: August 21, 2017
Final paper submission: September 8, 2017

=== Overview ===
The areas of Human-Robot Interaction (HRI) and robot learning are tightly
coupled. Interaction has been used to enhance robot learning from people,
providing methods for quickly learning new actions/tasks (e.g. learning
from demonstration), understanding constraints, bootstrapping computational
approaches, and providing context to what the robot learns. Similarly,
learning has been used to improve HRI, providing a means for the robot to
learn better models of social interaction,improve collaboration, and lead
to better overall interaction with respect to specified evaluation
metrics.  Learning for interaction also entails learning user models (e.g.
as providers of information) and user preferences for adaptability.

Contributions have been made in either subfield: (i) interaction to aid
learning and (ii) learning to interact. However, there is little work which
lies at their intersection. Additionally, work in one subfield may benefit
the other; synergy between these two research directions could result in a
robotic system which learns to better interact with humans and is thereby
more likely to achieve its learning goals. Our objective is to bring
together experts from these two topics (both learning for interaction and
interacting to aid learning).

=== Topics ===
Topics include, but are not limited to:

+ Interactive Learning
--- Learning from Demonstration
--- Imitation learning
--- Active learning
--- Human-guided model refinement
--- Interpreting human feedback for learning
+ Learning to Interact
--- Learning from noisy human demonstrations
--- Learning and acting according to user models/preferences
--- Learning social interaction models
--- Learning turn-taking or communication strategies
--- Measuring and optimising for user satisfaction
+ Intersection of Interactive Learning and Learning-guided Interaction
--- Learning for and from human-robot collaboration
--- Learning and expressing transparency during goal-directed interaction
--- Learning to act and interact from natural language

=== Call for Contributions ===
Full length paper: 6 pages max
Position paper: 6 pages max
Extended abstract: 2 pages max

=== Invited Speakers ===
Ross Knepper, Cornell University
Brian Scassellati, Yale University
Siddhartha Srinivasa, Carnegie Mellon University
Andrea Thomaz, University of Texas at Austin
Heni Ben Amor, Arizona State University
Hae Won Park, Massachusetts Institute of Technology

=== Organizers ===
Barış Akgün, Koç University
Kalesha Bullard, Georgia Institute of Technology
Vivian Chu, Georgia Institute of Technology
Tesca Fitzgerald, Georgia Institute of Technology
Matthew Gombolay, Massachusetts Institute of Technology
Chien-Ming Huang, Yale University
Brian Scassellati, Yale University
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