  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.
