This study presented a novel control approach for rehabilitation robotic system using the hybrid system theory and the subject's bio-damping and bio-stiffness parameters. Resistance training was selected as a paradigm. The proposed control architecture incorporated the physical therapist's behavior intervention, the stroke survivor's muscle strength changes, and the robotic device's motor control into a unified framework. The main focuses of this research were to (i) automatically monitor the subject's muscle strength changes using the online identified bio-damping/stiffness parameters; (ii) make decisions on the modification of the desired resistive force so as to coincide with the subject's muscle strength changes; and (iii) generate accommodating plans when the safety-related issues such as spasticity and the abnormal robotic working states happen during the execution of training tasks. A Barrett WAM compliant manipulator-based resistance training system and two experiments including four scenarios were developed to verify the proposed approach. Experimental results with healthy subjects showed that the hybrid system-based control architecture could administrate the subject's muscle strength changes and the robotic device's interventions in an automated and safe manner.