Dadu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipelines
文献类型：期刊论文
| 作者 | Sun, Wenhao2,3; Hou, Sai4; Wang, Zixuan1,3; Yu, Bo5; Liu, Shaoshan5; Yang, Xu4; Liang, Shuai2; Gan, Yiming2; Han, Yinhe2 |
| 刊名 | JOURNAL OF FIELD ROBOTICS
 |
| 出版日期 | 2025-12-10 |
| 页码 | 24 |
| 关键词 | closed-loop planning large language models memory augmentation robotic planning |
| ISSN号 | 1556-4959 |
| DOI | 10.1002/rob.70120 |
| 英文摘要 | Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are inefficient due to their large number of parameters and prone to inaccuracies because they operate in open-loop systems. We think the reason is that only applying LLMs as planners is insufficient. In this study, we propose Dadu-Embodied (Dadu-E), a robust closed-loop planning framework for embodied Artificial Intelligence (AI) robots. Specifically, Dadu-E is equipped with a relatively lightweight LLM, a set of encapsulated robot skill instructions, a robust feedback system, and memory augmentation. Together, these components enable Dadu-E to (i) actively perceive and adapt to dynamic environments, (ii) optimize computational costs while maintaining high performance, and (iii) recover from execution failures using its memory and feedback mechanisms. By seamlessly integrating a lightweight LLM, encapsulated robot skill instructions, closed-loop feedback, and memory augmentation, Dadu-E establishes a robust and extensible embodied AI robotic framework capable of stable operation in real-world environments. Extensive experiments on real-world and simulated tasks show that Dadu-E achieves task success rates comparable to embodied AI robots with larger models as planners like COME-Robot, while reducing computational requirements by . Users are encouraged to explore our system at https://rlc-lab.github.io/dadu-e/. |
| 资助项目 | National Key R&D Program of China[2024YFB4505800] ; National Natural Science Foundation of China[62025404] ; Beijing Municipal Science and Technology Commission (BMSTC)[Z241100004224015] ; Longgang District Shenzhen's Ten Action Plan for Supporting Innovation Projects[LGKCSDPT2024003] ; Longgang District Shenzhen's Ten Action Plan for Supporting Innovation Projects[LGKCSDPT2024004] ; Fundamental Research Funds for the Central Universities[2025XC11020] |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| WOS记录号 | WOS:001634516000001 |
| 出版者 | WILEY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42935]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Yu, Bo; Yang, Xu; Gan, Yiming; Han, Yinhe |
| 作者单位 | 1.Chinese Acad Sci CASIA, Inst Automat, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Chinese Acad Sci ICT, Beijing, Peoples R China 3.Univ Chinese Acad Sci UCAS, Sch Comp Sci & Technol, Sch Artificial Intelligence, Beijing, Peoples R China 4.Beijing Inst Technol BIT, Sch Comp Sci & Technol, Beijing, Peoples R China 5.Shenzhen Inst Artificial Intelligence & Robot Soc, Embodied AI Ctr, Shenzhen, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Wenhao,Hou, Sai,Wang, Zixuan,et al. Dadu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipelines[J]. JOURNAL OF FIELD ROBOTICS,2025:24. |
| APA | Sun, Wenhao.,Hou, Sai.,Wang, Zixuan.,Yu, Bo.,Liu, Shaoshan.,...&Han, Yinhe.(2025).Dadu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipelines.JOURNAL OF FIELD ROBOTICS,24. |
| MLA | Sun, Wenhao,et al."Dadu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipelines".JOURNAL OF FIELD ROBOTICS (2025):24. |
入库方式： OAI收割
来源：计算技术研究所
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