Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning

2025-07-16

AI Summary

This study systematically evaluates the role of visual input in multimodal large language models (MLLMs) for robot path planning, focusing on spatial reasoning, constraint satisfaction, and scalability bottlenecks. We introduce the first zero-shot and few-shot path planning benchmark for 2D grid environments, comprehensively evaluating 15 state-of-the-art MLLMs under varying grid scales to isolate the contributions of textual versus visual inputs. Results demonstrate that vision enhances path validity and optimality in small-scale grids but degrades markedly with increasing grid size—revealing fundamental limitations in long-range spatial modeling and hard-constraint adherence. Our core contribution is a reproducible evaluation framework that quantifies, for the first time, the complementary boundaries between visual and textual modalities in path planning and empirically identifies model scalability thresholds.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Motion and Path Planning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large Language Models (LLMs) show potential for enhancing robotic path planning. This paper assesses visual input's utility for multimodal LLMs in such tasks via a comprehensive benchmark. We evaluated 15 multimodal LLMs on generating valid and optimal paths in 2D grid environments, simulating simplified robotic planning, comparing text-only versus text-plus-visual inputs across varying model sizes and grid complexities. Our results indicate moderate success rates on simpler small grids, where visual input or few-shot text prompting offered some benefits. However, performance significantly degraded on larger grids, highlighting a scalability challenge. While larger models generally achieved higher average success, the visual modality was not universally dominant over well-structured text for these multimodal systems, and successful paths on simpler grids were generally of high quality. These results indicate current limitations in robust spatial reasoning, constraint adherence, and scalable multimodal integration, identifying areas for future LLM development in robotic path planning.

Problem

• Evaluating visual input's role in multimodal LLMs for robotic path planning
• Assessing performance of 15 multimodal LLMs in 2D grid path planning
• Identifying limitations in spatial reasoning and scalable multimodal integration

Innovation

• Benchmarking 15 multimodal LLMs for path planning
• Comparing text-only versus text-plus-visual inputs
• Evaluating scalability and spatial reasoning limitations