论文标题
人向机器人间接放置移交的抢占运动计划
Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers
论文作者
论文摘要
随着技术的发展，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。在任何情况下，最基本的协作任务之一是对象移交。人向机器人的交接可以采用两种方法中的一种：（1）直接掌握或（2）间接的手向安装到挑选。后一种方法可确保人与机器人之间的最小接触，但由于必须等待对象首先放置在表面上，因此也会导致空闲时间增加。为了最大程度地减少这种闲置时间，机器人必须先抢先预测对物体将要放置的人的意图。此外，要使机器人以任何形式的生产方式进行先发制人的行动，必须实时进行预测和运动计划。我们介绍了一种新颖的预测计划管道，该管道使机器人可以使用凝视和手势作为模型输入而先发行人类代理的预期位置。在本文中，我们研究了我们早期意图预测规范的表现和缺点，以及通过人类机器人案例研究使用此类管道的实际好处。
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.
