ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning

• URL: http://arxiv.org/abs/2403.15834v1
• Date: Sat, 23 Mar 2024 13:21:09 GMT
• Title: ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning
• Authors: Yiwen Chen, Yuyao Ye, Ziyi Chen, Chuheng Zhang, Marcelo H. Ang,
• Abstract summary: We introduce the Large Language Model Supervised Robotics Text2Skill Autonomous Learning framework. This framework aims to replace human participation in the robot skill learning process with large-scale language models. We provide evidence that our approach enables fully autonomous robot skill learning, capable of completing partial tasks without human intervention.
• Score: 19.337423880514717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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