Understanding the intentions of human teammates is critical for safe and effective human-robot interaction. The canonical approach for human-aware robot motion planning is to first predict the human's goal or path, and then construct a robot plan that avoids collision with the human. This method can generate unsafe interactions if the human model and subsequent predictions are inaccurate. In this work, we present an algorithmic approach for both arranging the configuration of objects in a shared human-robot workspace, and projecting ``virtual obstacles'' in augmented reality, optimizing for legibility in a given task. These changes to the workspace result in more legible human behavior, improving robot predictions of human goals, thereby improving task fluency and safety. To evaluate our approach, we propose two user studies involving a collaborative tabletop task with a manipulator robot, and a warehouse navigation task with a mobile robot.

翻译：理解人类队友的意图对于安全有效的人机交互至关重要。人类感知机器人运动规划的标准方法是首先预测人类的目标或路径，然后制定避免与人发生碰撞的机器人计划。如果人类模型及后续预测不准确，该方法可能产生不安全交互。本文提出一种算法方法，用于安排共享人机工作空间中物体的配置，并借助增强现实投射"虚拟障碍物"，以优化特定任务中的可读性。这些工作空间的调整使得人类行为更具可读性，从而改善机器人对人类目标的预测，提升任务流畅性与安全性。为评估该方法的有效性，我们进行了两项用户研究：一项涉及协作桌面操作任务（使用机械臂机器人），另一项涉及仓储导航任务（使用移动机器人）。