论文标题
同时进行的策略融合和系统识别，用于广义辅助控制
Concurrent Policy Blending and System Identification for Generalized Assistive Control
论文作者
论文摘要
在这项工作中，我们解决了解决具有多个不同参数的复杂协作机器人任务的问题。我们的方法将同时的策略融合与系统标识结合在一起，以创建对系统参数变化的强大策略。我们采用一个混合网络，该网络仅依赖于系统识别技术的参数估计。结果，此混合网络学习如何处理参数更改，而不是尝试学习如何同时解决通用参数集的任务。我们展示了计划在人类患有运动障碍的协作机器人和人类瘙痒任务上的能力。然后，与标准域随机化相比，我们通过各种系统识别技术展示了方法的效率。
In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with system identification to create generalized policies that are robust to changes in system parameters. We employ a blending network whose state space relies solely on parameter estimates from a system identification technique. As a result, this blending network learns how to handle parameter changes instead of trying to learn how to solve the task for a generalized parameter set simultaneously. We demonstrate our scheme's ability on a collaborative robot and human itching task in which the human has motor impairments. We then showcase our approach's efficiency with a variety of system identification techniques when compared to standard domain randomization.
