CALL FOR CONTRIBUTIONS AND PARTICIPATION ====================================================================== Synergies Between Learning and Interaction Full-Day Workshop at IROS 2017 Vancouver, B.C., Canada September 28, 2017 Workshop Website: https://sites.google.com/view/iros17sbli Submission Website: https://easychair.org/conferences/?conf=iros17sbli ====================================================================== === Important Dates === Submission deadline: July 21, 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 (Additional speakers TBA) === 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 --------------------------------------------------------------- For news of CHI books, courses & software, join CHI-RESOURCES mailto: [log in to unmask] To unsubscribe from CHI-ANNOUNCEMENTS send an email to mailto:[log in to unmask] For further details of CHI lists see http://listserv.acm.org ---------------------------------------------------------------