# RobotGPT: Robot Manipulation Learning from ChatGPT

**Authors:** Yixiang Jin, Dingzhe Li, Yong A, Jun Shi, Peng Hao, Fuchun Sun, Jianwei Zhang, Bin Fang  
**arXiv:** [2312.01421](https://arxiv.org/abs/2312.01421)  
**Date:** 2023-12-03  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2312.01421v1/

## Key points

- RobotGPT is a decision framework designed for robotic manipulation, focusing on stability and safety.
- It addresses limitations of using ChatGPT-generated code, which lacks stability and safety guarantees due to varying answers and unpredictability.
- RobotGPT incorporates an efficient prompt structure, robust learning model, and task difficulty metric for evaluating ChatGPT's performance in robot tasks.
- The goal is to harness ChatGPT's problem-solving abilities by training a reliable agent through RobotGPT.
- Setting the temperature parameter to 0 can enhance output consistency but may reduce diversity and creativity in responses.
- Using RobotGPT as an expert rather than a task planner enhances performance and reliability in robot manipulation tasks.
- Evaluation across simulation and real-world environments has shown significant improvements in task success rates compared to using ChatGPT directly.
- Developed by authors Yixiang Jin, Dingzhe Li, Yong A., Jun Shi, Peng Hao, Fuchun Sun, Jianwei Zhang, and Bin Fang, RobotGPT bridges the gap between AI problem-solving capabilities and practical robotic applications.

## Summary

RobotGPT is a revolutionary decision framework specifically designed for robotic manipulation. Its primary focus is on ensuring stability and safety in operations. This framework addresses the limitations of using ChatGPT-generated execution code, which lacks the ability to guarantee system stability and safety. This is due to its tendency to provide varying answers for the same task, leading to unpredictability. As a result, direct integration of ChatGPT into the robot manipulation loop becomes hindered. To address this issue, RobotGPT incorporates an efficient prompt structure and a robust learning model. It also introduces a metric for evaluating task difficulty in order to assess ChatGPT's performance in robot manipulation tasks. The ultimate goal of RobotGPT is to harness ChatGPT's problem-solving capabilities by training a dependable agent. While setting the temperature parameter to 0 can enhance output consistency, it also risks diminishing diversity and creativity in ChatGPT's responses. However, with RobotGPT as an expert rather than a task planner, training becomes a more stable approach that enhances overall performance and reliability in robot manipulation tasks. The effectiveness of RobotGPT has been evaluated across both simulation and real-world environments. The results have shown significant improvements in task success rates compared to using ChatGPT directly for code generation. The collaborative efforts of authors Yixiang Jin, Dingzhe Li, Yong A., Jun Shi, Peng Hao, Fuchun Sun, Jianwei Zhang, and Bin Fang have culminated in the development of RobotGPT: an innovative framework that bridges the gap between AI problem-solving capabilities and practical robotic applications.