NAMO-LLM: Efficient Navigation Among Movable Obstacles with Large Language Model Guidance

TL;DR

NAMO-LLM introduces a novel LLM-guided sampling-based planner for robot navigation among movable obstacles, effectively handling highly cluttered environments by reasoning about obstacle reconfiguration.

Contribution

The paper presents NAMO-LLM, a new LLM-guided sampling strategy that improves planning efficiency and scalability in complex NAMO scenarios.

Findings

Outperforms existing methods in runtime and plan quality.

Scales effectively to highly cluttered environments.

Proven to be probabilistically complete.

Abstract

Several planners have been proposed to compute robot paths that reach desired goal regions while avoiding obstacles. However, these methods fail when all pathways to the goal are blocked. In such cases, the robot must reason about how to reconfigure the environment to access task-relevant regions - a problem known as Navigation Among Movable Objects (NAMO). While various solutions to this problem have been developed, they often struggle to scale to highly cluttered environments. To address this, we propose NAMO-LLM, a sampling-based planner that searches over robot and obstacle configurations to compute feasible plans specifying which obstacles to move, where, and in what order. Its key novelty is a non-uniform sampling strategy guided by Large Language Models (LLMs) biasing the tree construction toward directions more likely to yield a solution. We show that NAMO-LLM is probabilistically complete and demonstrate through experiments that it efficiently scales to cluttered environments, outperforming related works in both runtime and plan quality.