类工业活动中人体运动可操作性的分析与迁移
Analysis and Transfer of Human Movement Manipulability in Industry-like Activities
摘要：
人类在执行不同类型的工业任务时表现出出色的学习、规划和适应能力。鉴于对任务要求的一些了解，人类能够计划他们的肢体运动，以预期特定技能的执行。例如，当操作者需要在表面上钻孔时，她的四肢的姿势变化以保证与钻孔任务规范兼容的稳定配置，例如施加正交于表面的力。因此，我们有兴趣在工业活动中分析人体手臂的运动模式。要做到这一点，我们建立我们的分析，所谓的可操作性椭球体，它捕获的姿势依赖的能力，执行运动，并沿不同的任务方向施加力。通过对人体运动可操作性的深入分析，我们发现椭球形状是任务相关的，往往比经典的可操作性指标提供更多的信息。此外，我们展示了如何可操作性模式可以转移到机器人通过学习的概率模型，并采用可操作性跟踪控制器，根据预定义的控制层次的任务规划和执行的行为。
英文摘要：
Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, humans are able to plan their limbs motion in anticipation of the execution of specific skills. For example, when an operator needs to drill a hole on a surface, the posture of her limbs varies to guarantee a stable configuration that is compatible with the drilling task specifications, e.g. exerting a force orthogonal to the surface. Therefore, we are interested in analyzing the human arms motion patterns in industrial activities. To do so, we build our analysis on the so-called manipulability ellipsoid, which captures a posture-dependent ability to perform motion and exert forces along different task directions. Through thorough analysis of the human movement manipulability, we found that the ellipsoid shape is task dependent and often provides more information about the human motion than classical manipulability indices. Moreover, we show how manipulability patterns can be transferred to robots by learning a probabilistic model and employing a manipulability tracking controller that acts on the task planning and execution according to predefined control hierarchies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
