Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially without storing or accessing previous task information. Unfortunately, current learning systems, e.g., neural networks, are prone to deviate the weights learned in previous tasks after training new tasks, leading to catastrophic forgetting, especially in a sequential multi-tasks scenario. To address this problem, in this paper, we propose to overcome catastrophic forgetting with the focus on learning a series of robotic tasks sequentially. Particularly, a novel hierarchical neural network's framework called Encoding Primitives Generation Policy Learning (E-PGPL) is developed to enable continual learning with two components. By employing a variational autoencoder to project the original state space into a meaningful low-dimensional feature space, representative state primitives could be sampled to help learn corresponding policies for different tasks. In learning a new task, the feature space is required to be close to the previous ones so that previously learned tasks can be protected. Extensive experiments on several simulated robotic tasks demonstrate our method's efficacy to learn control policies for handling sequentially arriving multi-tasks, delivering improvement substantially over some other continual learning methods, especially for the tasks with more diversity.