Considering Socially Scalable Human-Robot Interfaces

Abstract

Collaborative robots are becoming increasingly present in everyday life, with applications ranging food and parcel delivery, security, and more. They can offer great value propositions for organizations and consumers. However, most people lack knowledge of how to interact with robots, and many robots themselves necessitate formal training which can be inaccessible to many and thus not societally scalable. Further, there is a lack of existing work investigating interfaces designs that can support non-dyadic interactions consisting of two or more individuals interacting with a robot sequentially and simultaneously; such interactions will be common in real-world usage. This research explores the efficacy of natural language interfaces for human-robot interaction through a human experiment and post-experiment survey. Results show that the natural language interface can afford teams enhanced capabilities to share robot control and avoid errors relative to other interfaces, while also increasing user perceptions towards overall interaction.