Guiding Multiple Remote Users in Physical Tasks with Language-driven Robotic Telepresence

Remote assistance through robotic telepresence could involve both control and memory challenges, particularly in one expert to multiple workers situation. In this work, we proposed a novelty language-driven interface to facilitate remote collaboration through telepresence robots. Through operations and maintenance expert interviews and a scenario simulation study, we identified key pain points in executing one-expert-multiple-workers remote guidance using the telepresence robot and proposed two design goals, which together consist of five sub-design goals with corresponding features. These features were integrated into a standard telepresence robot, resulting in the development of a Collaborative LLM-based Embodied Assistant Robot, named CLEAR Robot. A controlled experiment simulating a remote assembly task of one to two demonstrated that, compared to the standard telepresence robot, CLEAR Robot significantly improved efficiency, reduced cognitive load, facilitated more balanced collaboration, and improved the user experience. We also discuss the impact of language-driven implicit interactions in multi-user collaboration and provide insights for designing robot systems that support one-expert-multiple-workers remote guidance in the future.