Robot task planning is an important problem for autonomous robots in long-horizon challenging tasks. As large pre-trained models have demonstrated superior planning ability, recent research investigates utilizing large models to achieve autonomous planning for robots in diverse tasks. However, since the large models are pre-trained with Internet data and lack the knowledge of real task scenes, large models as planners may make unsafe decisions that hurt the robots and the surrounding environments. To solve this challenge, we propose a novel Safe Planner framework, which empowers safety awareness in large pre-trained models to accomplish safe and executable planning. In this framework, we develop a safety prediction module to guide the high-level large model planner, and this safety module trained in a simulator can be effectively transferred to real-world tasks. The proposed Safe Planner framework is evaluated on both simulated environments and real robots. The experiment results demonstrate that Safe Planner not only achieves state-of-the-art task success rates, but also substantially improves safety during task execution. The experiment videos are shown in https://sites.google.com/view/safeplanner .

翻译：机器人任务规划是自主机器人在长期复杂任务中面临的重要问题。随着大型预训练模型展现出卓越的规划能力，近期研究致力于利用大模型实现机器人在多样化任务中的自主规划。然而，由于大模型基于互联网数据预训练，缺乏对真实任务场景的认知，其作为规划器可能做出危害机器人及周围环境的不安全决策。为解决这一挑战，我们提出了一种新颖的安全规划器框架，该框架通过增强大型预训练模型的安全意识，实现安全且可执行的规划。在此框架中，我们开发了一个安全预测模块来指导高层级的大模型规划器，该在仿真器中训练的安全模块能够有效迁移至现实世界任务。所提出的安全规划器框架在仿真环境与真实机器人上均进行了评估。实验结果表明，安全规划器不仅达到了最先进的任务成功率，还显著提升了任务执行过程中的安全性。实验视频展示于 https://sites.google.com/view/safeplanner。