Objectives

The areas of Human-Robot Interaction (HRI) and robot learning are tightly coupled. Interaction has been used to enhance robotic learning from people, providing methods for quickly learning new actions/tasks, 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, to improve collaboration, and lead to better overall interaction.

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 aim is to bring together experts from these two topics (both learning for interaction and interacting to aid learning). In doing so, we expect to:

• Identify interesting problems on each side that would relate to the other and explore how each contributes to goal-oriented social interaction, improved learning performance, and/or better collaboration
• Identify problems which are best addressed using a dual-focus on learning and interaction
• Develop a synergy between learning for interaction and interactive learning, identifying practical ways in which research in each direction can benefit from the other
• Brainstorm about potential future directions to take to address the duality of the identified research problems

Topics of Interest

• Interactive Learning
  • Learning from Demonstration
  • Imitation learning
  • Active learning
  • Human-guided model refinement
  • Interpreting human feedback for learning
• Learning to Interact
  • Learning and acting according to user models/preferences
  • Learning social interaction models
  • Learning turn-taking or communication strategies
  • Measuring and optimizing 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

Submissions

We invite several types of contributions:

• Full length paper: 6 pages max
• Position paper: 6 pages max
• Extended abstract: 2 pages max

References do not count towards the page limit.

Author Package: You can use the IEEE RAS Templates (Latex/Word)

Submission website: https://easychair.org/conferences/?conf=iros17sbli