发表机构
University of Science and Technology of China; University of North Carolina at Chapel Hill; University of Central Florida（中国科学技术大学; 北卡罗来纳大学教堂山分校; 中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出AID-SR多层框架，利用大型语言模型通过闭环物理反馈自动设计连续体机器人，在14个任务基准上实现96.2%模拟可行性和26.7%任务成功率，并完成真实世界验证。
AI 中文摘要
大型语言模型（LLMs）最近已成为从高级规范自动设计机器人的有前景工具，但对于在复杂物理交互下运行的机器人，它们仍然效果不佳。这一局限性源于基于语言的推理与具身物理后果之间的差距，常常导致设计出的机器人物理有效性较低。在这项工作中，我们提出了一个多层框架AID-SR，通过将模拟器观察到的物理状态转化为对LLM设计者的结构化反馈，建立了一个闭环。结合语义批评、人类反馈和迭代细化，该框架促进了物理可行且功能有意义的机器人设计的生成。我们在一个包含14个任务的基准上评估了我们的方法，这些任务涵盖到达、抓取、运动和操作，针对肌腱驱动的连续体机器人。所提出的框架在模拟可行性检查中达到了96.2%的通过率，并且通过应用常见的强化学习训练，26.7%的机器人能够成功完成相应任务。随后，我们制造了三个AID-SR设计的机器人，它们在现实世界中成功完成了任务。这些在模拟和现实环境中的广泛实验证明并打破了利用LLMs自动设计连续体机器人的壁垒。源代码和实验资源可在https URL公开获取。
英文摘要
Large language models (LLMs) have recently emerged as a promising tool for automating robot design from high-level specifications, yet they remain ineffective for robots operating under complex physical interactions. This limitation stems from the gap between language-based reasoning and the physical consequences of embodiment, often resulting in designs with low physical validity. In this work, we propose a multi-layered framework, AID-SR, that establishes a closed loop by translating simulator-observed physical states into structured feedback for the LLM designer. Combined with semantic critique, human feedback, and iterative refinement, the framework promotes the generation of physically feasible and functionally meaningful robot designs. We evaluate our approach on tendon-driven continuum robots across a benchmark of 14 tasks spanning reaching, grasping, locomotion, and manipulation. The proposed framework achieves 96.2% rate for passing the simulation feasibility check and by applying a common reinforcement learning training, 26.7% robots can successfully fulfill the corresponding task. We then fabricate three designed robots of AID-SR that successfully complete the task in real-world. These extensive experiments across simulation and real-world environments demonstrate and break the wall of utilizing the LLMs for automated design of continuum robots. The source code and experimental resources are publicly available at https://github.com/UNITES-Lab/AID-SR.
CommentsThe first three authors contributed equally to this work
Journal refProceedings of Robotics: Science and Systems XXII, 2026
DOI：10.15607/RSS.2026.XXII.194
