ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning
arxiv(2024)
摘要
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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