Robotics learning highly relies on human expertise and efforts, such as
demonstrations, design of reward functions in reinforcement learning,
performance evaluation using human feedback, etc. However, reliance on human
assistance can lead to expensive learning costs and make skill learning
difficult to scale. In this work, we introduce the Large Language Model
Supervised Robotics Text2Skill Autonomous Learning (ARO) framework, which aims
to replace human participation in the robot skill learning process with
large-scale language models that incorporate reward function design and
performance evaluation. We provide evidence that our approach enables fully
autonomous robot skill learning, capable of completing partial tasks without
human intervention. Furthermore, we also analyze the limitations of this
approach in task understanding and optimization stability.

本研究介绍了大规模语言模型辅助自主学习框架，旨在取代人类参与的机器人技能学习过程，并提供证据表明这种方法可以实现完全自主的机器人技能学习，能够在没有人类干预的情况下完成部分任务。此外，我们还分析了该方法在任务理解和优化稳定性方面的局限性。