  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
\href{https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main}{here}.

Robotics;Artificial Intelligence