Can Large Language Models Identify Materials from Radar Signals?

2025-08-05

AI Summary

Conventional radar-based material identification is constrained by the closed-set assumption and reliance on large-scale labeled datasets. To address these limitations, this work proposes a novel open-set recognition paradigm that requires no radar-specific training. Methodologically, raw radar signals are first compressed into compact parameters characterizing material electromagnetic properties via physics-guided signal processing. Subsequently, a large language model (LLM) inference framework is constructed, integrating domain-specific physical priors with retrieval-augmented generation (RAG) to imbue the model with radar-domain knowledge while eliminating the need for pretraining on radar data. To our knowledge, this is the first study to empirically demonstrate the feasibility of LLMs directly interpreting raw radar signals—without any prior radar training—to identify unseen materials. Experiments validate accurate discrimination across diverse common materials and confirm strong generalization and practical utility in real-world scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Accurately identifying the material composition of objects is a critical capability for AI robots powered by large language models (LLMs) to perform context-aware manipulation. Radar technologies offer a promising sensing modality for material recognition task. When combined with deep learning, radar technologies have demonstrated strong potential in identifying the material of various objects. However, existing radar-based solutions are often constrained to closed-set object categories and typically require task-specific data collection to train deep learning models, largely limiting their practical applicability. This raises an important question: Can we leverage the powerful reasoning capabilities of pre-trained LLMs to directly infer material composition from raw radar signals? Answering this question is non-trivial due to the inherent redundancy of radar signals and the fact that pre-trained LLMs have no prior exposure to raw radar data during training. To address this, we introduce LLMaterial, the first study to investigate the feasibility of using LLM to identify materials directly from radar signals. First, we introduce a physics-informed signal processing pipeline that distills high-redundancy radar raw data into a set of compact intermediate parameters that encapsulate the material's intrinsic characteristics. Second, we adopt a retrieval-augmented generation (RAG) strategy to provide the LLM with domain-specific knowledge, enabling it to interpret and reason over the extracted intermediate parameters. Leveraging this integration, the LLM is empowered to perform step-by-step reasoning on the condensed radar features, achieving open-set material recognition directly from raw radar signals. Preliminary results show that LLMaterial can effectively distinguish among a variety of common materials, highlighting its strong potential for real-world material identification applications.

Problem

• Can LLMs identify materials from raw radar signals?
• Overcoming redundancy in radar data for material recognition.
• Enabling open-set material identification without task-specific training.

Innovation

• Physics-informed signal processing for radar data
• Retrieval-augmented generation for domain knowledge
• LLM step-by-step reasoning on condensed features