Learning from Demonstration (LfD) allows novice users to teach robots through demonstrations without coding; however, such demonstrations are often suboptimal and can limit robot performance. To better support novices, we investigate the design of a feedback system that enables effective human-robot communication during demonstrations. We first conducted a focus group study (N = 9) to identify effective ways of visualizing key robot information, including joint limits, self-collisions, and manipulability. Guided by these insights, we designed an AR-based real-time feedback system and evaluated it in a between-subjects user study (N = 36) on a 7-DoF collaborative robot. Participants performed two tasks—insertion and pouring—with the second task enabling assessment of participants’ learning across tasks. Results show that real-time feedback reduced demonstration time, increased task completion rate, lowered perceived mental workload, and improved adherence to robot kinematic constraints. These findings demonstrate the effectiveness of the real-time feedback system for intuitive and effective robot teaching.
Best Paper Honourable Mention at the 2026 ACM Designing Interactive Systems Conference (DIS 2026).