论文标题
预期人类在对象抓握和放置任务中全身运动预测的意图
Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks
论文作者
论文摘要
非结构化环境中的运动预测是一个困难的问题，对于安全有效的人类机器人空间共享和协作至关重要。在这项工作中，我们专注于房屋，工作场所或餐馆等环境中的操纵运动，在这些环境中，可以利用整体任务和环境来产生准确的运动预测。在这些情况下，我们提出了一个算法框架，该算法框架基于可负担模型和经过运动捕获数据训练的短期人类动力学模型明确说明环境几何形状。我们提出了专用的功能网络，以提供可抓性和宽容性，并利用专用的RNN进行短期运动预测。基于约束的轨迹优化器使用抓地力和放置概率密度的预测，以在整个地平线上产生全身运动预测。我们通过与地面真实数据进行比较来表明，我们可以实现与使用Oracle Grasp和位置位置相似的全身运动预测的类似性能。
Motion prediction in unstructured environments is a difficult problem and is essential for safe and efficient human-robot space sharing and collaboration. In this work, we focus on manipulation movements in environments such as homes, workplaces or restaurants, where the overall task and environment can be leveraged to produce accurate motion prediction. For these cases we propose an algorithmic framework that accounts explicitly for the environment geometry based on a model of affordances and a model of short-term human dynamics both trained on motion capture data. We propose dedicated function networks for graspability and placebility affordances and we make use of a dedicated RNN for short-term motion prediction. The prediction of grasp and placement probability densities are used by a constraint-based trajectory optimizer to produce a full-body motion prediction over the entire horizon. We show by comparing to ground truth data that we achieve similar performance for full-body motion predictions as using oracle grasp and place locations.
