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、高效和协作的人类机器人团队的需求变得越来越重要。在任何环境下,最根本的协作任务之一是物体的交接。人类对机器人的交接可以采取两种方法:(1) 直接手对手,或(2) 间接手对手或手对脚的交接方式。后一种方法确保了人类与机器人之间最低限度的接触,但也可能导致更多的闲暇时间,因为必须等待物体首先落到表面。为了尽量减少这种闲置时间,机器人必须先发制人地预测该物体放置地点的人类意图。此外,机器人要以任何生产方式先发制人地采取行动,就必须实时作出预测和运动规划。我们引入一个新的预测规划管道,使机器人能够先发制人,用凝视和手势作为模型投入,朝人类代理人预定的安置地点前进。在本文中,我们研究了我们早期意图预测仪的性能和后退,以及通过人类机器人案例研究使用这种管道的实际好处。