Causal Robot Communication Inspired by Observational Learning Insights
This work addresses the need for robots to gain trust and acceptance through clear intent communication, representing an incremental application of existing psychological insights to a new domain.
The paper tackles the problem of enabling autonomous robots to communicate their decisions effectively by identifying and explaining causal actions in sequences, applying insights from observational learning in psychology to human-robot interaction for the first time.
Autonomous robots must communicate about their decisions to gain trust and acceptance. When doing so, robots must determine which actions are causal, i.e., which directly give rise to the desired outcome, so that these actions can be included in explanations. In behavior learning in psychology, this sort of reasoning during an action sequence has been studied extensively in the context of imitation learning. And yet, these techniques and empirical insights are rarely applied to human-robot interaction (HRI). In this work, we discuss the relevance of behavior learning insights for robot intent communication, and present the first application of these insights for a robot to efficiently communicate its intent by selectively explaining the causal actions in an action sequence.