Hagenow Michael, Senft Emmanuel, Radwin Robert, Gleicher Michael, Mutlu Bilge, Zinn Michael
Michael Hagenow and Michael Zinn are with the Department of Mechanical Engineering, University of Wisconsin-Madison, Madison 53706, USA.
Emmanuel Senft, Michael Gleicher, and Bilge Mutlu are with the Department of Computer Sciences, University of Wisconsin-Madison, Madison 53706, USA.
IEEE Robot Autom Lett. 2021 Apr;6(2):3720-3727. doi: 10.1109/lra.2021.3064500. Epub 2021 Mar 8.
Many tasks, particularly those involving interaction with the environment, are characterized by high variability, making robotic autonomy difficult. One flexible solution is to introduce the input of a human with superior experience and cognitive abilities as part of a shared autonomy policy. However, current methods for shared autonomy are not designed to address the wide range of necessary corrections (e.g., positions, forces, execution rate, etc.) that the user may need to provide to address task variability. In this paper, we present , where users provide corrections to key robot state variables on top of an otherwise autonomous task model. We provide an instantiation of this shared autonomy paradigm and demonstrate its viability and benefits such as low user effort and physical demand via a system-level user study on three tasks involving variability situated in aircraft manufacturing.
许多任务，尤其是那些涉及与环境交互的任务，具有高度变异性，这使得机器人自主性变得困难。一种灵活的解决方案是引入具有卓越经验和认知能力的人类输入，作为共享自主性策略的一部分。然而，当前的共享自主性方法并非旨在处理用户可能需要提供的广泛必要校正（例如位置、力、执行速率等），以应对任务变异性。在本文中，我们提出了一种方法，即用户在其他方面自主的任务模型之上，对关键机器人状态变量进行校正。我们提供了这种共享自主性范式的一个实例，并通过一项针对飞机制造中涉及变异性的三项任务的系统级用户研究，证明了其可行性和益处，如低用户工作量和体力需求。
