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

Abstract

This paper presents AnyBipe, a revolutionary framework that streamlines the entire robot learning workflow through Large Language Model (LLM) integration. AnyBipe addresses the critical challenge of accelerating robotics research by automating traditionally manual processes including reward design, training supervision, and sim-to-real validation.

Key Contributions

• Automated Reward Design: LLM-powered reward function generation eliminates manual engineering
• Training Supervision: Intelligent monitoring and adjustment of training processes
• Sim-to-Real Validation: Automated pipeline for transferring learned behaviors to real robots
• Foundational Toolchain: Establishes the infrastructure for developing complex autonomous agents

Technical Innovation

The AnyBipe framework serves as a “foundational toolchain” that enables researchers to focus on high-level algorithm design while automating the tedious aspects of robot learning pipeline construction. This acceleration is crucial for advancing the field of embodied AI and autonomous robotics.

Impact

By significantly reducing the time and expertise required for bipedal robot training, AnyBipe democratizes access to advanced robotics research and enables faster iteration cycles in autonomous agent development.

Status: Accepted for Oral Presentation at IROS 2025

Recommended citation: Yao, Y., He, W., Gu, C., Du, J., Tan, F., Zhu, Z., & Lu, J. (2025). "AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models." Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Oral Presentation.
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