  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
