Lang2Manip：一种基于LLM的符号到几何操作规划工具 (Lang2Manip: A Tool for LLM-Based Symbolic-to-Geometric Planning for Manipulation)

Simulation is essential for developing robotic manipulation systems, particularly for task and motion planning (TAMP), where symbolic reasoning interfaces with geometric, kinematic, and physics-based execution. Recent advances in Large Language Models (LLMs) enable robots to generate symbolic plans from natural language, yet executing these plans in simulation often requires robot-specific engineering or planner-dependent integration. In this work, we present a unified pipeline that connects an LLM-based symbolic planner with the Kautham motion planning framework to achieve generalizable, robot-agnostic symbolic-to-geometric manipulation. Kautham provides ROS-compatible support for a wide range of industrial manipulators and offers geometric, kinodynamic, physics-driven, and constraint-based motion planning under a single interface. Our system converts language instructions into symbolic actions and computes and executes collision-free trajectories using any of Kautham's planners without additional coding. The result is a flexible and scalable tool for language-driven TAMP that is generalized across robots, planning modalities, and manipulation tasks.

翻译：仿真是开发机器人操作系统，特别是任务与运动规划（TAMP）的关键，其中符号推理与基于几何、运动学和物理的执行过程进行交互。大型语言模型（LLM）的最新进展使机器人能够从自然语言生成符号规划，然而在仿真中执行这些规划通常需要针对特定机器人进行工程化或依赖于特定规划器的集成。在本工作中，我们提出了一种统一流程，将基于LLM的符号规划器与Kautham运动规划框架相连接，以实现可泛化的、与机器人无关的符号到几何操作。Kautham为广泛的工业机械臂提供ROS兼容支持，并在单一接口下提供基于几何、运动动力学、物理驱动和约束的运动规划。我们的系统将语言指令转换为符号动作，并使用Kautham中的任一规划器计算并执行无碰撞轨迹，无需额外编码。其结果是一个灵活且可扩展的语言驱动TAMP工具，能够跨机器人、规划模态和操作任务实现泛化。