  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.
