Calibrated Human-Robot Teaching: What People Do When Teaching Norms to Robots

Robots deployed in social communities must act according to the communities' social and moral norms. To acquire the large number of nuanced norms, robots can rely on human teaching. While humans tend to naturally use more than one teaching method when training a novice, current human-in-the-loop teaching frameworks have typically relied on single teaching methods (e.g., instruction or reward). To gain insight into how humans would teach robots to master social and moral norms, we present a novel paradigm in which participants interactively teach a simulated robot to behave appropriately in a healthcare setting, choosing to either instruct the robot or evaluate its proposed actions. We demonstrate that 89.5% of human teachers naturally use both teaching methods. Importantly, they adapt their teaching method as they observe the robot's task performance, responding dynamically to the task's difficulty, the robot's most recent action, and the accumulated evidence of the robot's learning progress.