We present RobotGPT, an innovative decision framework for robotic manipulation that prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee the stability and safety of the system. ChatGPT may provide different answers for the same task, leading to unpredictability. This instability prevents the direct integration of ChatGPT into the robot manipulation loop. Although setting the temperature to 0 can generate more consistent outputs, it may cause ChatGPT to lose diversity and creativity. Our objective is to leverage ChatGPT's problem-solving capabilities in robot manipulation and train a reliable agent. The framework includes an effective prompt structure and a robust learning model. Additionally, we introduce a metric for measuring task difficulty to evaluate ChatGPT's performance in robot manipulation. Furthermore, we evaluate RobotGPT in both simulation and real-world environments. Compared to directly using ChatGPT to generate code, our framework significantly improves task success rates, with an average increase from 38.5% to 91.5%. Therefore, training a RobotGPT by utilizing ChatGPT as an expert is a more stable approach compared to directly using ChatGPT as a task planner.

翻译：我们提出RobotGPT，一种面向机器人操作创新的决策框架，该框架优先考虑稳定性和安全性。ChatGPT生成的执行代码无法保证系统的稳定性和安全性，且对同一任务可能提供不同答案，导致不可预测性。这种不稳定性阻碍了ChatGPT直接集成到机器人操作循环中。虽然将温度参数设置为0可生成更一致的输出，但可能导致ChatGPT丧失多样性和创造性。我们的目标是利用ChatGPT在机器人操作中的问题解决能力，训练一个可靠代理。该框架包含有效的提示结构和鲁棒的学习模型。此外，我们引入了一种衡量任务难度的指标以评估ChatGPT在机器人操作中的表现，并在仿真与真实环境中对RobotGPT进行测试。相较于直接使用ChatGPT生成代码，我们的框架显著提升了任务成功率，平均值从38.5%提升至91.5%。因此，将ChatGPT作为专家来训练RobotGPT，比直接将其作为任务规划器更为稳定。