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RobotGPT: Robot Manipulation Learning from ChatGPT (2312.01421v1)

Published 3 Dec 2023 in cs.RO

Abstract: 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.

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Citations (24)
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Summary

  • The paper introduces RobotGPT, a framework that leverages ChatGPT's problem-solving abilities to generate and correct robotic manipulation code.
  • RobotGPT integrates structured prompt engineering with a self-correction mechanism that tests and refines code in simulation for enhanced reliability.
  • Experimental results demonstrate a significant improvement in task success, rising from 38.5% with direct ChatGPT use to 91.5% using RobotGPT.

Introduction

LLMs have been making impressive strides across various fields, including but not limited to text generation, machine translation, and code synthesis. Specifically, there's been a growing interest in integrating LLMs with robotic systems, especially for robot system planning and Human-Robot Interaction (HRI). This integration aims to enable users to interact with robots in a more natural way using natural language. Despite the progress made in natural language interaction with robots, challenges remain in terms of the system's stability and understandability when using LLMs alone.

Objectives and Framework

At the core of this research is the goal to utilize the advanced problem-solving capabilities provided by LLMs, particularly ChatGPT, for robot manipulation learning. However, the unpredictability and variability of responses from ChatGPT hinder its direct application for generating robot execution code due to concerns over stability and safety. This paper introduces RobotGPT, an innovative framework that combines prompt engineering, learning models, and evaluation metrics to guide ChatGPT in generating and correcting code for robot manipulation tasks. RobotGPT aims at leveraging ChatGPT's capabilities while training a reliable agent that can ensure consistent and safe execution of tasks.

Methodology

To address the unpredictability in ChatGPT's responses, the authors propose a structured prompting method that helps ChatGPT understand the task and environment more clearly for robot manipulation. Additionally, a novel self-correction mechanism is integrated to rectify any erroneous outputs from ChatGPT. This error correction phase involves executing the generated code line by line within a simulator. Any runtime errors that occur are analyzed for corrections, which are then fed back to ChatGPT for prompt adjustments and regeneration of corrected code.

For evaluating the success of tasks completed as per ChatGPT-generated code, an automatic evaluation bot within the framework checks for the correctness of code and task completion. The robot learning process utilizes the state-of-the-art robotic manipulation benchmark and learning framework called BulletArm to train an agent from the ChatGPT-generated demonstrations, ensuring a stable performance across various tasks regardless of their complexity levels.

Experiments and Outcomes

The paper discusses extensive evaluations of RobotGPT in both simulation and real-world environments. Compared to direct usage of ChatGPT for code generation, which had a task success average of 38.5%, the framework significantly improved task success rates to 91.5% on average. Additionally, the research conducts an AB test where RobotGPT is benchmarked against humans in solving complex tasks that require understanding and interaction with objects in the real world.

In conclusion, the paper finds that training a robot using RobotGPT by leveraging ChatGPT as an expert exhibits an enhanced approach with improved stability and performance in completing manipulation tasks. The AB test further reveals that robots powered by LLMs like ChatGPT can outperform non-LLM-based methods, particularly in tasks requiring broad knowledge. This demonstrates the potential benefits of combining the problem-solving prowess of LLMs with robotic manipulation.

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