AnyBipe: An Automated End-to-End Framework for Training and Deploying Bipedal Robots Powered by Large Language Models

Abstract

Training and deploying reinforcement learning (RL) policies for robots is a complex task, requiring careful design of reward functions, sim-to-real transfer, and performance evaluation across various robot configurations. These tasks traditionally demand significant human expertise and effort. To address these challenges, this paper introduces Anybipe, a novel, fully automated, end-to-end framework for training and deploying bipedal robots, leveraging large language models (LLMs) for reward function generation, while supervising model training, evaluation, and deployment. The framework integrates comprehensive quantitative metrics to assess policy performance, deployment effectiveness, and safety. Additionally, it allows users to incorporate prior knowledge and preferences, improving the accuracy and alignment of generated policies with expectations. We demonstrate how Anybipe reduces human labor while maintaining high levels of accuracy and safety, examined on three different bipedal robots, showcasing its potential for autonomous RL training and deployment.