To enable the robot avoid obstacles in real-time while performing complex tasks, an interactive learning approach for robot obstacle avoidance based on DP-KMP is proposed. First, the whole framework of this approach is constructed, which adopts the segmentation-generalization strategy to implement the learning of demonstrated trajectories with rapid segmentation and the planning of sub-trajectories for obstacle avoidance. During the learning phase, a trajectory segmentation strategy based on the DP algorithm is proposed to improve the efficiency of segmentation, while a Gaussian mixture model strategy is used to extract the reference database from each sub-trajectory. In the trajectory planning phase, the KMP model is used to implement the trajectory reproduction and generalization, while the reference database update strategy based on human-robot interaction feedback is introduced to enhance the success rate of obstacle avoidance through human-robot interaction. Aiming at the issue that this update strategy may fail to plan a successful obstacle avoidance trajectory, two available conditions are proposed for inspecting the sub-trajectories generated by segmentation. Finally, the effectiveness of mentioned available conditions is verified by simulation. Experimental results show that the proposed approach segments the demonstrated trajectories in just 0. 084 and 0. 107 s for two different experiments, respectively. Additionally, the KUKA cobot successfully avoids all static and suddenly changing obstacles through multiple interactions with user during the execution of the different lift-place tasks.