How Non-experts Kinesthetically Teach a Robot over Multiple Sessions: Diversity in Teaching Styles and Effects on Performance

In real-world applications, robots should adapt to users and environments; however, users may not know how to teach new tasks to a robot. We studied whether participants without any experience in teaching a robot would become more proficient robot teachers through repeated kinesthetic human-robot teaching interactions. An experiment was conducted with twenty-eight participants who were asked to kinesthetically teach a humanoid robot different cleaning tasks in five repeated sessions, each session including four tasks. Throughout the sessions, participants' gaze patterns, methods of manipulating the robot's arm, their perceived workload, and some physical properties of the demonstrated actions were measured. Our data analyses revealed a diversity in non-experts' human-robot teaching styles in repeated interactions. Three clusters of human teachers were identified based on participants' performance in providing the demonstrations. The majority of participants significantly improved their success and speed of kinesthetic demonstrations by performing multiple rounds of teaching the robot. Overall, participants gazed less often at the robot's hand and perceived less effort over repeated sessions. Our findings highlight how non-experts adapt to robot teaching by being exposed repeatedly to human-robot teaching tasks, without any formal training or external intervention, and we identify the characteristics of successful and improving human teachers.