On the Effect of Robot Errors on Human Teaching Dynamics

TL;DR

This study investigates how robot errors influence human teaching behaviors, revealing that errors lead to longer teaching times, more detailed feedback, and changes in feedback modality, which can inform better interface and algorithm design.

Contribution

It provides the first human-centered analysis of how robot errors impact teaching dynamics, highlighting key behavioral changes in human instructors.

Findings

Increased teaching time with robot errors

More detailed feedback when errors are present

Robot errors influence feedback modality choice

Abstract

Human-in-the-loop learning is gaining popularity, particularly in the field of robotics, because it leverages human knowledge about real-world tasks to facilitate agent learning. When people instruct robots, they naturally adapt their teaching behavior in response to changes in robot performance. While current research predominantly focuses on integrating human teaching dynamics from an algorithmic perspective, understanding these dynamics from a human-centered standpoint is an under-explored, yet fundamental problem. Addressing this issue will enhance both robot learning and user experience. Therefore, this paper explores one potential factor contributing to the dynamic nature of human teaching: robot errors. We conducted a user study to investigate how the presence and severity of robot errors affect three dimensions of human teaching dynamics: feedback granularity, feedback richness, and teaching time, in both forced-choice and open-ended teaching contexts. The results show that people tend to spend more time teaching robots with errors, provide more detailed feedback over specific segments of a robot's trajectory, and that robot error can influence a teacher's choice of feedback modality. Our findings offer valuable insights for designing effective interfaces for interactive learning and optimizing algorithms to better understand human intentions.