  A significant challenge for the control of a robotic lower extremity
rehabilitation exoskeleton is to ensure stability and robustness during
programmed tasks or motions, which is crucial for the safety of the
mobility-impaired user. Due to various levels of the user's disability, the
human-exoskeleton interaction forces and external perturbations are
unpredictable and could vary substantially and cause conventional motion
controllers to behave unreliably or the robot to fall down. In this work, we
propose a new, reinforcement learning-based, motion controller for a lower
extremity rehabilitation exoskeleton, aiming to perform collaborative squatting
exercises with efficiency, stability, and strong robustness. Unlike most
existing rehabilitation exoskeletons, our exoskeleton has ankle actuation on
both sagittal and front planes and is equipped with multiple foot force sensors
to estimate center of pressure (CoP), an important indicator of system balance.
This proposed motion controller takes advantage of the CoP information by
incorporating it in the state input of the control policy network and adding it
to the reward during the learning to maintain a well balanced system state
during motions. In addition, we use dynamics randomization and adversary force
perturbations including large human interaction forces during the training to
further improve control robustness. To evaluate the effectiveness of the
learning controller, we conduct numerical experiments with different settings
to demonstrate its remarkable ability on controlling the exoskeleton to
repetitively perform well balanced and robust squatting motions under strong
perturbations and realistic human interaction forces.
