LAUSR

This letter presents an adaptive optimal control strategy for developing assist-as-needed robotic rehabilitation. The primary goal is to encourage patient participation and increase the effectiveness of training sessions by minimizing robot intervention while following a predefined path. To achieve this, the problem is modeled as a two-player non-zero-sum game, with cooperative and individual objectives for the human and robot specified as different cost functions. Policy iteration techniques are adopted for learning the optimal solution online. The shared autonomy feature is specifically achieved through seamless adaptation of the robot's autonomy according to its estimation of human intention. The performance of the proposed approach is illustrated in several simulations and experimental studies.