# MLLM-Search: A Zero-Shot Approach to Finding People using Multimodal Large Language Models

arXiv: https://arxiv.org/abs/2412.00103 · HTML: https://mlsanity.com/paper/2412.00103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models](https://mlsanity.com/paper/2406.01584/) (2024)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://mlsanity.com/paper/2304.03439/) (2023)
- [PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs](https://mlsanity.com/paper/2402.07872/) (2024)
- [L3MVN: Leveraging Large Language Models for Visual Target Navigation](https://mlsanity.com/paper/2304.05501/) (2023)
- [OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models](https://mlsanity.com/paper/2402.10670/) (2024)
- [Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models](https://mlsanity.com/paper/2402.12058/) (2024)
- [NavFormer: A Transformer Architecture for Robot Target-Driven Navigation in Unknown and Dynamic Environments](https://mlsanity.com/paper/2402.06838/) (2024)
- [Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions](https://mlsanity.com/paper/2110.02181/) (2021)
- [Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations](https://mlsanity.com/paper/2203.00187/) (2022)
- [Robots Understanding Contextual Information in Human-Centered Environments using Weakly Supervised Mask Data Distillation](https://mlsanity.com/paper/2012.08282/) (2020)
- [LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models](https://mlsanity.com/paper/2402.08774/) (2024)

## Built on by (most cited first, 4 of 4)

- [Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach](https://mlsanity.com/paper/2502.00114/) (2025)
- [SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models](https://mlsanity.com/paper/2511.12972/) (2025)
- [Generate, Analyze, and Refine: Training-Free Sound Source Localization via MLLM Meta-Reasoning](https://mlsanity.com/paper/2604.06824/) (2026)
- [Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans](https://mlsanity.com/paper/2608.16514/) (2026)

## Abstract (arXiv)

Robotic search of people in human-centered environments, including healthcare settings, is challenging as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person's plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints by a topological graph and regions by semantic labels. This is incorporated into a MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario, and a waypoint planner which generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to finding a person in a new unseen environment.