This workshop focuses on issues surrounding human-robot interaction for robot self-assessment of system proficiency. For example, how should a robot convey predicted ability on a new task? How should it report performance on a task that was just completed? Communities in both computer science and robotics have addressed questions of introspection to monitor system performance and adjust behavior to guarantee or improve performance. Self-assessment can range from simple detection of proficiency up through evaluation, explanation, and prediction. Robots need the ability to make assessments and communicate them a priori, in situ, and a posteriori in order to support effective autonomy and utilization by human partners and supervisors. This is a pressing challenge for human-robot interaction for a variety of reasons. Prior work has shown that robot expression of performance can alter human perception of the robot and decisions on control allocation. There is also significant evidence in
robotics that accurately setting human expectations is critical, especially when proficiency is below human expectations. Therefore, more knowledge is needed on how systems should communicate specifics about current and future task competence.
We welcome contributions focused on assessing, explaining, and conveying robot proficiency to human teammates.
We are requesting 2 page (position) and 6 page (regular) papers using the HRI 2020 format. Anonymization is not required. When emailing the paper, please include the author list, affiliations, and email addresses in the body of the email.
Regular papers should focus on research results. All submissions will be peer-reviewed and authors of accepted papers will be asked to do either a poster or podium presentation at the workshop. At least one author of each accepted paper must register for the workshop.
We will send acceptance notifications by February 21. Authors of accepted papers will be offered the option of having their papers uploaded to a workshop-specific archive on arxiv.org. Inclusion in this archive will not be mandatory since it may create problems for authors who wish to submit follow-on work to venues with strict prior publication rules.
Submission Deadline: January 30, 2020, 23:59 Anywhere on Earth (AoE)
Submit via email to: [hidden email]
We look forward to your participation,
Aaron Steinfeld, Carnegie Mellon University, [hidden email] Michael Goodrich, Brigham Young University, [hidden email]