Large language models (LLMs) have gained increasing popularity in robotic task planning due to their exceptional abilities in text analytics and generation, as well as their broad knowledge of the world. However, they fall short in decoding visual cues. LLMs have limited direct perception of the world, which leads to a deficient grasp of the current state of the world. By contrast, the emergence of visual language models (VLMs) fills this gap by integrating visual perception modules, which can enhance the autonomy of robotic task planning. Despite these advancements, VLMs still face challenges, such as the potential for task execution errors, even when provided with accurate instructions. To address such issues, this paper proposes a ReplanVLM framework for robotic task planning. In this study, we focus on error correction interventions. An internal error correction mechanism and an external error correction mechanism are presented to correct errors under corresponding phases. A replan strategy is developed to replan tasks or correct error codes when task execution fails. Experimental results on real robots and in simulation environments have demonstrated the superiority of the proposed framework, with higher success rates and robust error correction capabilities in open-world tasks. Videos of our experiments are available at https://youtu.be/NPk2pWKazJc.