  This paper aims to develop a new human-machine interface to improve
rehabilitation performance from the perspective of both the user (patient) and
the machine (robot) by introducing the co-adaption techniques via model-based
reinforcement learning. Previous studies focus more on robot assistance, i.e.,
to improve the control strategy so as to fulfill the objective of
Assist-As-Needed. In this study, we treat the full process of robot-assisted
rehabilitation as a co-adaptive or mutual learning process and emphasize the
adaptation of the user to the machine. To this end, we proposed a Co-adaptive
MDPs (CaMDPs) model to quantify the learning rates based on cooperative
multi-agent reinforcement learning (MARL) in the high abstraction layer of the
systems. We proposed several approaches to cooperatively adjust the Policy
Improvement among the two agents in the framework of Policy Iteration. Based on
the proposed co-adaptive MDPs, the simulation study indicates the
non-stationary problem can be mitigated using various proposed Policy
Improvement approaches.
