Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs

TL;DR

This paper presents a multi-robot system that uses 3D scene graphs and large language models to understand natural language commands, build shared maps, and execute complex tasks in outdoor environments.

Contribution

It introduces a novel multi-robot framework integrating 3D scene graphs with LLM-based planning for natural language instruction execution.

Findings

Effective real-time relocalization and planning in outdoor environments

Successful translation of natural language commands into executable plans

Demonstrated system scalability in large-scale outdoor settings

Abstract

In this paper, we introduce a multi-robot system that integrates mapping, localization, and task and motion planning (TAMP) enabled by 3D scene graphs to execute complex instructions expressed in natural language. Our system builds a shared 3D scene graph incorporating an open-set object-based map, which is leveraged for multi-robot 3D scene graph fusion. This representation supports real-time, view-invariant relocalization (via the object-based map) and planning (via the 3D scene graph), allowing a team of robots to reason about their surroundings and execute complex tasks. Additionally, we introduce a planning approach that translates operator intent into Planning Domain Definition Language (PDDL) goals using a Large Language Model (LLM) by leveraging context from the shared 3D scene graph and robot capabilities. We provide an experimental assessment of the performance of our system on real-world tasks in large-scale, outdoor environments. A supplementary video is available at https://youtu.be/8xbGGOLfLAY.