论文标题
将原始政策融合在共享控制中的辅助遥控器中
Blending Primitive Policies in Shared Control for Assisted Teleoperation
论文作者
论文摘要
运动原始人具有适应机器人状态变化的特性，同时保持对原始政策的吸引力。因此，我们通过考虑到原始策略的状态偏差是由用户输入引起的。随着原始性从用户输入中恢复，它隐含地将人类和机器人策略融合在一起，而无需其权重（称为仲裁）。在本文中，我们采用动力运动原始功能（DMP），使我们避免需要进行多次演示，并且足够快以实现许多实例化，这是人类意图的每个假设。用户研究介绍了实现多个目标和动态障碍的辅助近距离任务。在显着降低人类干预措施的同时，达到了与常规近距离的可比性能，通常超过60％。
Movement primitives have the property to accommodate changes in the robot state while maintaining attraction to the original policy. As such, we investigate the use of primitives as a blending mechanism by considering that state deviations from the original policy are caused by user inputs. As the primitive recovers from the user input, it implicitly blends human and robot policies without requiring their weightings -- referred to as arbitration. In this paper, we adopt Dynamical Movement Primitives (DMPs), which allow us to avoid the need for multiple demonstrations, and are fast enough to enable numerous instantiations, one for each hypothesis of the human intent. User studies are presented on assisted teleoperation tasks of reaching multiple goals and dynamic obstacle avoidance. Comparable performance to conventional teleoperation was achieved while significantly decreasing human intervention, often by more than 60%.
