Informative Communication of Robot Plans
This work addresses the challenge of making robot communication more efficient for human users, though it appears incremental as it builds on existing theory of mind concepts.
The paper tackles the problem of robots verbalizing plans ineffectively by proposing a strategy that measures information gain against a user's prior knowledge, resulting in users understanding the robot's goal much quicker than with incremental or other order-based strategies.
When a robot is asked to verbalize its plan it can do it in many ways. For example, a seemingly natural strategy is incremental, where the robot verbalizes its planned actions in plan order. However, an important aspect of this type of strategy is that it misses considerations on what is effectively informative to communicate, because not considering what the user knows prior to explanations. In this paper we propose a verbalization strategy to communicate robot plans informatively, by measuring the information gain that verbalizations have against a second-order theory of mind of the user capturing his prior knowledge on the robot. As shown in our experiments, this strategy allows to understand the robot's goal much quicker than by using strategies such as increasing or decreasing plan order. In addition, following our formulation we hint to what is informative and why when a robot communicates its plan.