Improving Responsiveness to Robots for Tacit Human-Robot Interaction via Implicit and Naturalistic Team Status Projection

TL;DR

This paper introduces a novel implicit team status projection method called TPS that enhances human-robot responsiveness and team situation awareness in tacit interactions by reducing interruptions and cognitive load.

Contribution

The paper presents a new TPS method for implicit team status projection that improves responsiveness and team awareness in human-robot tacit teaming scenarios.

Findings

TPS significantly improves human responsiveness to robots.

Participants found robots with TPS more acceptable and favorable.

TPS maintains accurate team situation awareness effectively.

Abstract

Fluent human-human teaming is often characterized by tacit interaction without explicit communication. This is because explicit communication, such as language utterances and gestures, are inherently interruptive. On the other hand, tacit interaction requires team situation awareness (TSA) to facilitate, which often relies on explicit communication to maintain, creating a paradox. In this paper, we consider implicit and naturalistic team status projection for tacit human-robot interaction. Implicitness minimizes interruption while naturalness reduces cognitive demand, and they together improve responsiveness to robots. We introduce a novel process for such Team status Projection via virtual Shadows, or TPS. We compare our method with two baselines that use explicit projection for maintaining TSA. Results via human factors studies demonstrate that TPS provides a more fluent human-robot interaction experience by significantly improving human responsiveness to robots in tacit teaming scenarios, which suggests better TSA. Participants acknowledged robots implementing TPS as more acceptable as a teammate and favorable. Simultaneously, we demonstrate that TPS is comparable to, and sometimes better than, the best-performing baseline in maintaining accurate TSA