This paper proposes an offline and runtime combined framework for the autonomous motion of snake robots. With the dynamic feedback of its state during runtime, the robot utilizes the linear regression to update its control parameters for better performance and thus adaptively reacts to the environment. To reduce interference from infeasible samples and improve efficiency, the data set for runtime training is chosen from one in several clusters categorized from samples collected in offline practice. Moreover, only the most sensitive control parameter is updated at one iteration for better robustness and efficiency. The effectiveness and efficiency of our approach are evaluated by a set of case studies of pole climbing. Experimental results demonstrate that with the proposed framework, the snake robot can adapt its locomotion gait to poles with different unknown diameters.