# MMP-A*: Multimodal Perception Enhanced Incremental Heuristic Search on Path Planning

arXiv: https://arxiv.org/abs/2601.01910 · HTML: https://mlsanity.com/paper/2601.01910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [LLaVA-CoT: Let Vision Language Models Reason Step-by-Step](https://mlsanity.com/paper/2411.10440/) (2024)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks](https://mlsanity.com/paper/2503.09572/) (2025)
- [Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://mlsanity.com/paper/2109.06129/) (2021)
- [PlanGenLLMs: A Modern Survey of LLM Planning Capabilities](https://mlsanity.com/paper/2502.11221/) (2025)
- [Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation](https://mlsanity.com/paper/2407.05890/) (2024)
- [LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning](https://mlsanity.com/paper/2403.11552/) (2024)
- [Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning](https://mlsanity.com/paper/2310.03249/) (2023)
- [Guiding Long-Horizon Task and Motion Planning with Vision Language Models](https://mlsanity.com/paper/2410.02193/) (2024)
- [LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning](https://mlsanity.com/paper/2407.02511/) (2024)
- [CoPAL: Corrective Planning of Robot Actions with Large Language Models](https://mlsanity.com/paper/2310.07263/) (2023)
- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [Large Language Models for Planning: A Comprehensive and Systematic Survey](https://mlsanity.com/paper/2505.19683/) (2025)
- [PlanLLM: Video Procedure Planning with Refinable Large Language Models](https://mlsanity.com/paper/2412.19139/) (2024)
- [Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning](https://mlsanity.com/paper/2311.13720/) (2023)
- [Look Further Ahead: Testing the Limits of GPT-4 in Path Planning](https://mlsanity.com/paper/2406.12000/) (2024)
- [Evaluating Vision-Language Models as Evaluators in Path Planning](https://mlsanity.com/paper/2411.18711/) (2024)
- [LLM-Enhanced Path Planning: Safe and Efficient Autonomous Navigation with Instructional Inputs](https://mlsanity.com/paper/2412.02655/) (2024)
- [VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation](https://mlsanity.com/paper/2505.23267/) (2025)
- [Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning](https://mlsanity.com/paper/2507.12391/) (2025)
- [Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization](https://mlsanity.com/paper/2507.20923/) (2025)
- [Robust Mobile Robot Path Planning via LLM-Based Dynamic Waypoint Generation](https://mlsanity.com/paper/2501.15901/) (2025)

## Abstract (arXiv)

Autonomous path planning requires a synergy between global reasoning and geometric precision, especially in complex or cluttered environments. While classical A* is valued for its optimality, it incurs prohibitive computational and memory costs in large-scale scenarios. Recent attempts to mitigate these limitations by using Large Language Models for waypoint guidance remain insufficient, as they rely only on text-based reasoning without spatial grounding. As a result, such models often produce incorrect waypoints in topologically complex environments with dead ends, and lack the perceptual capacity to interpret ambiguous physical boundaries. These inconsistencies lead to costly corrective expansions and undermine the intended computational efficiency. We introduce MMP-A*, a multimodal framework that integrates the spatial grounding capabilities of vision-language models with a novel adaptive decay mechanism. By anchoring high-level reasoning in physical geometry, the framework produces coherent waypoint guidance that addresses the limitations of text-only planners. The adaptive decay mechanism dynamically regulates the influence of uncertain waypoints within the heuristic, ensuring geometric validity while substantially reducing memory overhead. To evaluate robustness, we test the framework in challenging environments characterized by severe clutter and topological complexity. Experimental results show that MMP-A* achieves near-optimal trajectories with significantly reduced operational costs, demonstrating its potential as a perception-grounded and computationally efficient paradigm for autonomous navigation.