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.
View on arXiv@article{sun2025_2503.08338,
title={ Trinity: A Modular Humanoid Robot AI System },
author={ Jingkai Sun and Qiang Zhang and Gang Han and Wen Zhao and Zhe Yong and Yan He and Jiaxu Wang and Jiahang Cao and Yijie Guo and Renjing Xu },
journal={arXiv preprint arXiv:2503.08338},
year={ 2025 }
}