“Me and you together” movement impact in multi-user collaboration tasks

This paper presents a study on collaborative manipulation between an autonomous robot and multiple users. We investigate how different motion types impact people's ability to understand the robot's goals in a multi-user scenario. We propose an approach based on Collaborative Probabilistic Movement Primitives to generate the robot's movements, exploiting predictability and legibility of movement to express intentions through motion. We compare the impact on the interaction of using only either predictable or legible movements, and propose a third approach - hybrid motion - that selects, in each situation, whether to execute a predictable motion or a legible motion, depending on what the robot perceives as more efficient for the multi-user collaboration effort. To test the impact of the three motion types in the context of a collaborative task, we run a user study using a Baxter robot that autonomously serves cups of water to three users upon request. Our results show that, in the particular case where all users simultaneously request water, the hybrid motion performs better than the other two.