Like much of AI, research into automated planning has, for the most part, focused on planning a course of actions for autonomous agentsacting in isolation. Humans--if allowed in the loop at all--were mostly used as a crutch to improve planning efficiency. The significant current interest in human-machine collaboration scenarios brings with it a fresh set of planning challenges for a planning agent, including the need to model and reason about the capabilities of the humans in the loop, the need to recognize their intentions so as to provide proactive support, the need to project its own intentions so that its behavior is explainable to the humans in the loop, and finally the need for evaluation metrics that are sensitive to human factors. These challenges are complicated by the fact that the agent has at best highly incomplete models of the intentions and capabilities of the humans. In this talk, I will discuss these challenges in adapting/extending planning technology to support teaming and cohabitation between humans and automated agents. I will then describe our recent research efforts to address these challenges, including novel planning models that,while incomplete, are easier to learn; planning and plan recognition techniques that can leverage these incomplete models to provide stigmergic and proactive assistance, while exhibiting "explainable"behaviors. I will conclude with an evaluation of these techniques within human-robot teaming scenarios.