Lang2Manip: A Tool for LLM-Based Symbolic-to-Geometric Planning for Manipulation

TL;DR

This paper introduces Lang2Manip, a versatile tool that connects language-based symbolic planning with geometric motion planning, enabling robot-agnostic manipulation tasks through a unified, scalable pipeline.

Contribution

It presents a unified pipeline integrating LLM-based symbolic planning with Kautham motion planning, enabling generalizable, robot-agnostic manipulation without additional coding.

Findings

Supports a wide range of industrial manipulators.

Enables collision-free trajectory computation from natural language instructions.

Provides a flexible, scalable framework for language-driven robotic manipulation.

Abstract

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.