Generalizable articulated object manipulation is essential for home-assistant robots. Recent efforts focus on imitation learning from demonstrations or reinforcement learning in simulation, however, due to the prohibitive costs of real-world data collection and precise object simulation, it still remains challenging for these works to achieve broad adaptability across diverse articulated objects. Recently, many works have tried to utilize the strong in-context learning ability of Large Language Models (LLMs) to achieve generalizable robotic manipulation, but most of these researches focus on high-level task planning, sidelining low-level robotic control. In this work, building on the idea that the kinematic structure of the object determines how we can manipulate it, we propose a kinematic-aware prompting framework that prompts LLMs with kinematic knowledge of objects to generate low-level motion trajectory waypoints, supporting various object manipulation. To effectively prompt LLMs with the kinematic structure of different objects, we design a unified kinematic knowledge parser, which represents various articulated objects as a unified textual description containing kinematic joints and contact location. Building upon this unified description, a kinematic-aware planner model is proposed to generate precise 3D manipulation waypoints via a designed kinematic-aware chain-of-thoughts prompting method. Our evaluation spanned 48 instances across 16 distinct categories, revealing that our framework not only outperforms traditional methods on 8 seen categories but also shows a powerful zero-shot capability for 8 unseen articulated object categories. Moreover, the real-world experiments on 7 different object categories prove our framework's adaptability in practical scenarios. Code is released at https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main.

翻译：通用化关节物体操作是家庭辅助机器人研究的关键方向。现有工作主要集中于基于示范的模仿学习或仿真环境中的强化学习，然而由于真实世界数据收集的高昂成本与精确物体仿真的技术瓶颈，这些方法在跨不同类型关节物体的广泛适应性方面仍面临挑战。近期研究尝试利用大语言模型（LLMs）强大的上下文学习能力实现通用化机器人操作，但多数工作聚焦于高层任务规划，忽视了底层运动控制。本文基于"物体的运动学结构决定操作方式"这一核心理念，提出运动学感知提示框架，通过向LLMs注入物体的运动学知识生成底层运动轨迹路径点，支持多样化物体操作。为有效向LLMs传递不同物体的运动学结构，我们设计统一运动学知识解析器，将各类关节物体表征为包含运动学关节与接触位置的统一文本描述。基于该统一描述，我们提出运动学感知规划器模型，通过设计的运动学感知思维链提示方法生成精确三维操作路径点。在涵盖16个不同类别共48个实例的评估中，本框架不仅对8个已知类别物体表现优于传统方法，更在8个未知类别关节物体上展现出强大的零样本迁移能力。此外，针对7种不同类别物体的实机实验验证了本框架在真实场景中的适应性。代码已开源至https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main。