As technology advances, the need for safe, efficient, and collaborative human-robot-teams has become increasingly important. One of the most fundamental collaborative tasks in any setting is the object handover. Human-to-robot handovers can take either of two approaches: (1) direct hand-to-hand or (2) indirect hand-to-placement-to-pick-up. The latter approach ensures minimal contact between the human and robot but can also result in increased idle time due to having to wait for the object to first be placed down on a surface. To minimize such idle time, the robot must preemptively predict the human intent of where the object will be placed. Furthermore, for the robot to preemptively act in any sort of productive manner, predictions and motion planning must occur in real-time. We introduce a novel prediction-planning pipeline that allows the robot to preemptively move towards the human agent's intended placement location using gaze and gestures as model inputs. In this paper, we investigate the performance and drawbacks of our early intent predictor-planner as well as the practical benefits of using such a pipeline through a human-robot case study.

翻译：随着技术的进步，对安全、高效且协作的人机团队的需求日益凸显。在任何场景中，最基本的协作任务之一是物体传递。人向机器人的传递可采用两种方式：(1) 直接手对手传递，或 (2) 间接手放至某处再拾取传递。后者可确保人与机器人之间的接触最小化，但也会因需等待物体先被放置在平面上而导致空闲时间增加。为最大程度减少此类空闲时间，机器人必须抢占式地预测人类意图，即物体将被放置的位置。此外，为使机器人能够以任何高效的方式抢占式行动，预测与运动规划必须实时进行。我们提出了一种新颖的预测-规划流水线，该流水线以注视和手势作为模型输入，使机器人能够抢占式地朝向人类操作员的意图放置位置移动。本文通过一项人机案例研究，探讨了我们的早期意图预测-规划器的性能与不足，以及使用此类流水线的实际优势。