In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient’s active rehabilitation willingness, the rehabilitation therapist will use the robot-assisted training strategy for patients who gradually have the ability to generate active force. This study proposed a motor function assessment technology for human upper-limb based on fuzzy recognition on interaction force and human-robot interaction control strategy based on assistance-as-needed. A passive training mode based on the calculated torque controller and an assisted training mode combined with the potential energy field were designed, and then the interactive force information collected by the three-dimensional force sensor during the training process was imported into the fuzzy inference system, the degree of active participation σ was proposed, and the corresponding assisted strategy algorithms were designed to realize the adaptive adjustment of the two modes. The significant correlation between the degree of active participation σ and the surface electromyography signals (sEMG) was found through the experiments, and the method had a shorter response time compared to a control strategy that only adjusted the mode through the magnitude of interaction force, making the robot safer during the training process.