论文标题
扩展对象的人类机器人共同操作：数据驱动的模型和人为二元分析的控制
Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads
论文作者
论文摘要
人类团队能够轻松执行协作操作任务。但是，对于机器人和人来说，同时操纵扩展对象是使用文献中现有方法的艰巨任务。本文我们的方法是使用人类二元实验的数据来确定我们用于物理人类机器人共同操作任务的运动意图。我们首先介绍并分析执行共同操作任务的人类二元组的数据。我们表明，与加速物体所需的力相比，人类人类二元数据具有有趣的趋势，包括相互作用力是不可忽略的，并且横向运动的开始的特征是来自Dyad领导者的明显扭矩触发器。我们还检查了不同的指标以量化不同二元组的性能。我们还基于人类试验的运动数据开发了一个深层神经网络，以根据过去的运动来预测人类意图。然后，我们展示如何将力和运动数据用作人类机器人二元组机器人控制的基础。最后，我们将两个控制器的人类共同操作的性能与人类人类二元的表现进行了比较。
Human teams are able to easily perform collaborative manipulation tasks. However, for a robot and human to simultaneously manipulate an extended object is a difficult task using existing methods from the literature. Our approach in this paper is to use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We first present and analyze data from human-human dyads performing co-manipulation tasks. We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad. We also examine different metrics to quantify performance of different dyads. We also develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion. We then show how force and motion data can be used as a basis for robot control in a human-robot dyad. Finally, we compare the performance of two controllers for human-robot co-manipulation to human-human dyad performance.
