融合强化学习与大模型，让人形机器人更懂指令、会规划、能适应复杂环境。
Trinity: A Modular Humanoid Robot AI System
- 整合强化学习、大语言模型和视觉语言模型，构建模块化智能系统。
- 实现基于自然语言指令的长期任务规划与复杂环境下的稳定控制。
- 适合研究人形机器人智能控制与具身智能的开发者与学者。
近年来，人形机器人研究受到越来越多关注。随着各类人工智能算法的突破，以人形机器人为代表的具身智能备受期待。强化学习（RL）算法的进步显著提升了人形机器人的运动控制与泛化能力；同时，大语言模型（LLM）和视觉语言模型（VLM）的突破为机器人带来了更多可能性。LLM使机器人能理解复杂语言指令并执行长期任务规划，而VLM则大幅增强了其对环境的理解与交互能力。本文提出 extcolor{magenta}{Trinity}——一种新型人形机器人人工智能系统，集成RL、LLM与VLM。通过融合这些技术，Trinity实现了在复杂环境中对人形机器人的高效控制。该创新方法不仅拓展了机器人能力边界，也为未来人形机器人研究与应用开辟了新路径。
原文摘要 · Abstract (English)
In recent years, research on humanoid robots has garnered increasing attention. With breakthroughs in various types of artificial intelligence algorithms, embodied intelligence, exemplified by humanoid robots, has been highly anticipated. The advancements in reinforcement learning (RL) algorithms have significantly improved the motion control and generalization capabilities of humanoid robots. Simultaneously, the groundbreaking progress in large language models (LLM) and visual language models (VLM) has brought more possibilities and imagination to humanoid robots. LLM enables humanoid robots to understand complex tasks from language instructions and perform long-term task planning, while VLM greatly enhances the robots' understanding and interaction with their environment. This paper introduces \textcolor{magenta}{Trinity}, a novel AI system for humanoid robots that integrates RL, LLM, and VLM. By combining these technologies, Trinity enables efficient control of humanoid robots in complex environments. This innovative approach not only enhances the capabilities but also opens new avenues for future research and applications of humanoid robotics.
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