There is great potential for humans and autonomous robots, each possessing their own capabilities and strengths, to perform tasks collaboratively across a number of domains, achieving greater performance than either could on their own. As is true for human-human teams, however, human-robot teams require a great deal of coordination. In shared tasks complex enough to see emergent benefits from teamwork, high-performing teams tend to possess well-aligned mental models regarding the task and each member's role within it, quickly communicating to rectify those models during times of mismatched expectation. To achieve the same benefits in human-robot teams requires a similar fluency of communication. However, since robots and humans reason in vastly different planning spaces, communicating effectively is non-trivial. Robot plans and rationale are often derived from mathematical optimization, which is difficult for human teammates to understand. Likewise, human decision-making patterns are difficult to quantify and are subject to significant noise, hindering their usefulness for optimization-based planners. Team fluency can be greatly improved by bridging human and robot task representations within the context of communication. In this thesis, I will discuss my research developing novel systems, algorithms, and interfaces for explicitly synchronizing mental models via agent-to-agent communication during live human-robot collaboration, spanning tasks ranging from tabletop manipulation to environment navigation and search. In particular, I will focus on spatially-grounded communication methods (augmented reality-based visualization presented in-situ at key locations within a shared environment, and natural language communication tied to such spatially-grounded features). Such methods leverage shared context between human and robot teammates, allowing for compact bi-directional communication of environment and task information, thus facilitating the alignment of mental models between agents and improving objective and subjective measures of team performance.