The lower exoskeleton system has attracted considerable interest in walking assistance of paraplegic patients. A critical issue in the walking assistance lower exoskeleton is how to generate gait motions for paraplegic patients. Predefined gait trajectory planning methods are widely used owing to their simplicity and effectiveness. However, a predefined gait trajectory planning method has three main drawbacks: (1) it requires a different gait model for different patients, (2) it cannot adapt to different terrains, such as slopes and stairs, (3) it does not consider the stability of the human exoskeleton system. In this study, we modeled the walking assistance lower exoskeleton with paraplegic patients as a human exoskeleton hybrid agent (HEHA). On the basis of the HEHA model, an adaptive gait planning method with dynamic movement primitives is proposed; in this method, the center of mass of HEHA is considered to ensure the stability of the human exoskeleton system. To adapt different pilots in slope scenarios, the reinforcement learning method is employed to update the parameters of the proposed gait model. The experimental results in both the simulation environment and the real-time exoskeleton system show that the proposed gait planning method makes the human exoskeleton system more stable in uphill slope scenarios.