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.

翻译：在这项工作中,我们解决了在多种不同参数下解决复杂的协作机器人任务的问题。 我们的方法是同时将政策与系统识别结合起来,以制定与系统参数变化相适应的通用政策。 我们使用一个混合网络,其国家空间完全依赖于系统识别技术的参数估计。 结果,这个混合网络学会了如何处理参数变化,而不是试图学习如何解决同时设定的通用参数的任务。 我们展示了我们的方法在合作机器人和人类切痒任务上的能力,其中人类有运动缺陷。 然后我们展示了我们的方法效率,与标准域随机化相比,我们用各种系统识别技术。