Informative Communication of Robot Plans

TL;DR

This paper introduces an informative verbalization strategy for robots that considers user prior knowledge to improve understanding of robot plans, outperforming simple incremental approaches.

Contribution

It proposes a novel plan verbalization method based on information gain and theory of mind, enhancing communication efficiency.

Findings

Faster understanding of robot goals using the proposed strategy

The strategy effectively identifies what is informative to communicate

Outperforms simple plan order strategies in experiments

Abstract

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.