RF-MatID: Dataset and Benchmark for Radio Frequency Material Identification
Authors: Xinyan Chen
Qinchun Li
Ruiqin Ma
Jiaqi Bai
Li Yi
Jianfei Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Material identification
Radio frequency (RF) sensing
Dataset
Benchmarking
Primary area: datasets and benchmarks

Abstract: Accurate material identification plays a crucial role in embodied AI systems, enabling a wide range of applications. However, current vision-based solutions are limited by the inherent constraints of optical sensors, while radio-frequency (RF) approaches, which can reveal intrinsic material properties, have received growing attention. Despite this progress, RF-based material identification remains hindered by the lack of large-scale public datasets and the limited benchmarking of learning-based approaches. In this work, we present RF-MatID, the first open-source, large-scale, wide-band, and geometry-diverse RF dataset for fine-grained material identification. RF-MatID includes 16 fine-grained categories grouped into 5 superclasses, spanning a broad frequency range from 4 to 43.5 GHz, and comprises 142k samples in both frequency- and time-domain representations. The dataset systematically incorporates controlled geometry perturbations, including variations in incidence angle and stand-off distance. We further establish a multi-setting, multi-protocol benchmark by evaluating state-of-the-art deep learning models, assessing both in-distribution performance and out-of-distribution robustness under cross-angle and cross-distance shifts. The 5 frequency-allocation protocols enable systematic frequency- and region-level analysis, thereby facilitating real-world deployment. RF-MatID aims to enable reproducible research, accelerate algorithmic advancement, foster cross-domain robustness, and support the development of real-world application in RF-based material identification.

Published as a conference paper at ICLR 2026
RF-MATID: DATASET AND BENCHMARK FOR RADIO FREQUENCY MATERIAL IDENTIFICATION
Xinyan Chen': Qinchun Li", Ruiqin M Bai", Li YI?, Jianfei Yang"
Nanyang Technological University 2 Ibaraki University
ABSTRACT
Accurate material identification plays crucial role in embodied AI systems, en- abling wide range of applications. However, current vision-based solutions are limited by the inherent constraints of optical sensors, while frequency (RF) approaches, which can reveal intrinsic material properties, have received growing attention. Despite this progress, RF-based material identification remains hin- dered by the lack of large-scale public datasets and the limited benchmarking of learning- based approaches. In this work, we present RF-MauI, the first open- source, large-scale, wide-band, and geometry-diverse RF dataset for fine-grainee material identification. RF-MatlD includes 16 fine-grained categories grouped into 5 superclasses, spanning a broad frequency range from 4 to 43.5 GHz, and comprises 142k samples in both frequency- and time-domain representations. The dataset systematically incorporates controlled geometry perturbations, including variations in incidence angle and stand-off distance. We further establish multi- setting, multi-protee prttiippotoool benchmark by evaluating state-of-the-a deep learning models, assessing both in-distribution performance and out-of-distribution robust- ness under cross-angle and cross distance shifts. The frequency-allie alllccctionno pro- tocols enable systematic frequency- and region-level analysis, thereby facilitating real-world deployment. RF-MauD aims to enable reproducible research, accel- erate algorithmic advancement, foster cross-domain robustness, and support the development of real-world application in RF-based material identification.
INTRODUCTION
Material identification is the task of identifying an object's physical category (e.g., metal, plastic) from its intrinsic properties, which is fundamental for physical artificial intelligence (Physical AI), particularly in embodied intelligence. Embodied intelligence arises from an agent's 's physical interac- tion with the environment, enabling it to perceive, reason, and adapt. Accurate material identification allows an embodied agent to infer object properties within its operational range, guiding manipu- lation and functional interactions. This capability has broad implications across diverse domains, such as enabling embodied agents to interpret fine- fine-grained object attributes for physical scene un- derstanding through material perception (Xiao et al., 2018), and to ground complex reasoning and control in material-driven functional affordances (Do et al., 2018)
Currently, material identification relies mainly on optical sensors, such as cameras and hyperspec- tral sensors (Drehwald et al., 2023; Xue et al., 2017; Schmid et al., 2023), to capture distinguishable spatial characteristics (e.g. texture, edges) (Erickson et al., 2020) and light spectrum features (e.g. reflectance and transmittance values) (Zahiri et al., 2022). However, vision-based material identi- fication is inherently constrained by the visual similarity of fine-grained categories (e.g.. steel vs. aluminum), limited robustness under real-world perturbations such as lighting and perspectives, and the inability of sensors to reveal intrinsic physical properties, including elasticity and conductivity.
To overcome the inherent constraints of optical sensors, non-visual sensing modalities, such radio frequency (RF), are gaining attention as they exploit electromagnetic interactions to reveal intrinsic
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material properties beyond surface appearance (Khushaba & Hill, 2022; Hägele et al., 2025). How- ever, RF-based material identification has not yet been extensively explored. There are no publicly accessible large-scale datasets, hindering reproducibility and fair benchmarking across algorithms. In addition, commercial off-the-shelf (COTS) sensors offer limited and fragmented frequency cov- erage (Yang al., 2024), hindering systematic evaluation of material identification across the RF spectrum, which is crucial for selecting optimal operational bands in various applications. Finally, most studies are conducted in controlled laboratory settings and rarely incorporate perturbations such as variations in sensor -ojject geometry (e.g.. incidence angle, stand-off- off-dfstacce)) leaving open questions regarding robustness and scalability deployment scenarios.
Thus, we present RF-MatID, a large-scale, wide-band, and geometry-divers RF dataset designed to advance fine-grann aneeggrined material identification and enable the development of more robust Physical Al algorithms. RF-MatlD encompasses 16 carefully curated fine-grained categories, systematically derived from 5 superclasses to capture subtle intra-class variations. The dataset spans a broad fre- quency spectrum from 4 to 43.5 GHz, sampled at 53 points per GHz. Through large-scale data acqui- sition, RF-MatID comprises 142k samples represented in both the frequency and time domains (7 lk samples in each). Note that these 142k samples correspond to representation-leve instances derived from 71k unique physical measurements, each provided with a paired frequency- and -nee-domain representation. RF-MatID also provides 5 frequency protocols (including protocols compliant with legal frequency regulations in major global economies) and 7 split settings for a comprehensive benchmark. The key contributions and characteristics of RF-MaiD are summarized follows:
First open-source, wide-band, geometry-diverse RF dataset for fine-grained material identi- fication. To the best of our knowledge, we construct the first large-scale open-source RF dataset covering 16 fine-grained material categories from 5 superclasses. The dataset spans wide fre- quency band (4-43.5 GHz), and systematically incorporates variations in incidence angle and stand-off distance emulate realistic geometric conditions. Both time-domain and frequency- domain representations are provided, enabling multi melltipprrspectvee evaluation.
Investigation of RF data representations and protocol-level applicability. We evaluate RF data representations by comparing classification accuracy on raw frequency- and processed time- domain signals, showing that raw frequency domain data can be directly leveraged by deep learn- ing models without additional domain transformation. Additionally, we explore RF-based material identification under various frequency frequency-allocation allocation protocols to assess the practicality of deploying RF systems in compliance with regulatory constraints. We further evaluate consecutive sub-bands of different bandwidths to gain insights into frequency selection across diverse applications.
Benchmark of learning based approaches and robustness. We establish extensive benchmarks of state-of-the-a deep learning models on the RF material identification task, adapting architec- tures from computer vision, natural language processing, and RF sensing. Beyond in in-distribution accuracy, we present a systematic evaluation of out-of out-of--istribne -f-distribution robustness in RF sensing, through cross-angle and cross-distance domain shifts that emulate geometric perturbations.
2 RELATED WORK
2.1 MATERIAL IDENTIFICATION
Material identification has been extensively studied due to its importance in industrial (Johns et al., 2023) and civil (Ha et al., 2018) contexts in the previous decades. In the signal processing domain, physics-based approaches have been well studied for material identification (Wolff, 1990; Wu et al., 2020: Rothwell et al., 2016; Sahin et al., 2020), which employ signal processing techniques such as the Fresnel equations (Fresnel, 1834) and the Nicolson- Roon--Rss-WWiiii Weir (NRW) method (Nicolson & Ross, 2007) to estimate intrinsic material electromagnetic properties (e.g- permittivity, conductiv- ity, and absorption characteristics) for materials distinguishing. However, physics-basee approaches often rely on idealized assumptions and hand-crafted formulas, introducing challenges such as sen- sitivity environmental conditions, limited adaptability to diverse application scenarios, and sus- ceptibility to extraneous object-specific factors unrelated to the material(e.g thickness and size).
With the advancement of deep learning techniques, learning-base based approaches provide more accu- rate and generalizable solutions for material identification. Vision-based approaches constitute the predominant paradigm for material identification. These methods exploit visual features, such as
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Dataset
Modality
Frequency (GHH)
Band- Width
Super- # Sub- Classes Classes
Geometric Variations
Public Accessiility
# Benchmark Models
Samples
Ha et al.
0,5-1.0
0.5
1
16
distance, angle
2,048
RFID
Wang et al.
0.92-0.93
0.01
2
16
distance, angle
1,800
Feng et al.
5.0-5.8
0.8
10
distance
2,000
X
Wi-Fi
Shi et al.
2.4-2.5
0.1
4
14
1,568
Dhekne al.
3.5-5.5
2.0
1
33
330
Zheng al.
6.5-8.0
1.5
distance
14,000
x
Wu et ul.
58,7-61.3
2.6
5
21
distance
67,200
He et al.
mm Wave
77,0-81.0
4.0
5
ungle
200,000
Shanbhag et
77.0-810
4.0
7
23
distance, angle
15,386
X
Chen
77.0-810
4.0
distance, angle
129,600
UWB-
RF-MatID
4.0-43.5
39.5
5
16
distance, angle
142,000
mmm Wave
Table Comparisons of RF-MaiI with other RF material identification datasets. Previous datasets are organized by sensing modality and '-' in the table denotes the absence of corresponding dataset feature in the research work.
color, texture, and reflectance properties, allowing computer vision models to learn hierarchical rep- resentations that reveal the underlying material patterns. Driven by the ubiquitous deployment of optical sensors, numerous material image datasets and benchmarks have been established (Wein- mann al., 2014; Bell al., 2015), enabling standardized and reproducible research and improving model accuracy and generalizability (Drehwald et al., 2023; Xue et al., 2017). Beyond RGB-based solutions, other studies have explored more physically informative optical modalities: for instance, TOF cameras capture combination of surface and subsurface scattering effects (Su et al., 2016), while hyperspectral imaging records full spectral profile at each image pixel (Salas et al., 2025). However, the weak penetration of near-infrared camera signals, which are typically in the MHz range, limits their ability to capture information beyond an object's surface. As a result, vision-based material identification is highly susceptible to variations in lighting and object geometry, which has spurred greater interest in robust RF-based approaches.
Existing RF-based deep learning approaches for material identification can be broadly divided into catture-based (two-stage) methods and end-to-end learning methods. Feature-based methods extend traditional physics-drive approaches by applying machine learning classifiers neered signal features for material identification (He et al., 2022; Shanbhag et al., 2023). While such classifiers can capture latent feature patterns beyond hand-crafted rules, their performance mains constrained by the quality of engineered features and shows limited adaptability to diverse real-world conditions. To overcome the limitations, recent works explore end-to-end deep learning frameworks that directly operate on RF signals, enabling discriminative representation learning of material characteristics without explicit feature extraction. (Zhang al., 2024; Hägele al., 2025).
manually engi-
2.2 RADIO-PRRQUENCY MATERIAL. DATASETS
Currently, most RF-based material datasets are collected using wireless COTS sensors operating in the 0.9-81 GHz frequency range, capturing electromagnetic signals that encode material proper- ties (Chen et al., 2024). Accordingly, these datasets can be broadly categorized based on the sensing modality into four clusters. collected in the 0.90-0.927 GHz frequency band, and the target object is either tagged on the surface or placed between a reader and tags deployed in the environment (Wang al., 2017: Ha al., 2020). The modality enables applications such as through- wall sensing via strong low-frequency penetra- tion, but the reliance on physical tags greatly complicates data collection. Wi-Fi-based datasets primarily leverage the 2.4 GHz and 5 GHz frequency bands, and the target object is typically placed between a Wi-Fi transmitter and receiver (Yang et al., 2023; Feng et al., 2019; Shi et al., 2021). The ubiquity of Wi-Fi devices facilitates convenient data collection, but the narrow bandwidth and distortions induced by hardware imperfections constrain the reliability of signals. (UWB)-based datasets mostly collected using altra-wideban pulse signals in the 3. 3..660.6 GHz frequency range and the equipment setup is similar to WiFi-based collection. UWB-base sys- tems can capture fine-grained attenuation and delay properties (Dhekne al., 2018; Zheng al.,
(RFID)-based datasets
mostly
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2021), but face practical barriers from strict synchronization demands and high deployment costs. Millimeter-aan (Mm -bwde datasets are mainly collected using COTS devices operating in the 57-64 GHz and 76-81 GHz frequency bands (Wu et al., 2020; Shanbhag et al., 2023; Chen et al., 2025). A single mm Wave radar unit is an integrated transceiver that provides accurate phase information characterizing the dielectric properties of the materials. However, its limited penetration confines the sensing to material surfaces, making it highly sensitive to environmental occlusions.
However, we observe that none of the existing datasets are publicly accessible, resulting in a lack of standardized state-of-the-art comparisons across the latest algorithms. Moreover, due to limitations of COTS sensors, each dataset typically covers only a narrow frequency band, lacking diversity in the frequency domain. In addition, most datasets lack systematic benchmarks to evaluate the performance of modern deep learning models on RF-based material identification tasks. To ad- dress these gaps, we propose RF-MatID, the first open-source dataset and benchmark covering 39.5 GHz UWB B- 8-mm Wave (Zhang & Pan, 2013) frequency band, including 16 fine-grained material cate- gories, and providing comprehensive benchmarks with 9 models, 5 evaluation protocols, and 7 data split settings. RF-MatID is critical for advancing machine learning research in RF-based material identification and can facilitate developments in embodied AI for tasks such indoor scene under- standing, precise robotic manipulation, and affordance learning. Table summarizes both previously used RE-based material datasets and our proposed RF-MatlD dataset.
3 PRELIMINARIES OF RF SENSING
3.1 RF DATA PROPERTIES
Various RF-based material sensing systems, summarized in section 2.2, can be broadly catego- rized into radar-based and radar-based approaches. Benefiting from coherent transceiver de- sign, radar-based sensing provides high-resolution amplitude and phase information, that can serve as discriminative features for learning- basedd material classification in indoor embodied Al tasks. Leveraging these advantages, we establish a UWB-mm Wave sensing platform designed to drive real-world applications. In RF-MatlD'ss mono-static sensing system, electromagnetic waves transmitted from an antenna, interact with the material subject, and reflect back to the antenna. When these waves encounter a material, portion of their energy is reflected at the surface, while the remainder is transmitted into the material. The material's intrinsic physical properties, such as permittivity and conductivity, will affect the amplitude, phase, and temporal characteristics of the electromagnetic waves. These variations form informative latent features for machine learning models, enabling fine-grained material identification. Furthermore, the behavior of electromagnetic waves is influenced by its frequency: lower-frequency waves penetrate deeper and reveal bulk prop- erties, whereas lossy materials. Thus, system collects signal data spanning both the centimeter-wan band (3-30 GHz) and the millimeter- wave Q-band (30-50 GHz), ensuring complementary information capture for robust learning across diverse materials.
waves are more sensitive to surface details but attenuate more in
In our RF-based material sensing setup, the acquired data consists of complex signals uniformly sampled across the 4-43.5 GHz band. The spectrum is discretized into 2,048 frequency bins, each corresponding to a distinct carrier frequency. As illustrated in the formula below, the response at each frequency fi is represented by its in-phase (I) and quadrature (Q) components, forming a complex value.
H(f)) = I(fa+ (Q(f)) = H (H()) ( (ZM(S))
(1)
Here, the magnitude (fs) encodes amplitude attenuation and the phase (H())) encodes the propagation delay introduced by the material. These complementary features form the raw sensor measurements, but standard deep learning backbones operate in the real domain. This discrep- ancy motivated the exploration of efficient RF representations, as presented in section 4.2, enabling learning-bas based models to effectively leverage complementary amplitude and phase information for fine-grained material identification.
3.2 RF TOOLS AND PLATFORM
To facilitate the data acquisition of our RF-MatID, we develop a customized RF data collection plat- form. As shown in Figure (a)), we employ RF SPIN DRH40 (RFSpin, 2024) double ridged horn
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(a)
(b)
material subject
Vector Network Analyarr
convex lons
RF sensor
Figure 1:
Data collection setup: (a) the customized sensing platform and (b) the acquisition layout.
antenna to transmit and receive signals across 4-40 GHz. The signals are subsequently processed by 1-port vector network analyzer (MS46131A) (Anritsu, 2025), operating over MHz-43.. GHz, data. The sensor's sensing range is ~22 m, which is inten-
to generate the raw frequency-doman tionally tailored for indoor robot manipulation tasks in embodied Al that require high-precision, close-range perception. This design choice facilitates applications where the sensor is mounted on a robotic end-effector to enable learning and executing adaptive grasping based on material proper- ties. In the acquisition platform, position the RF sensor a height-adjustable stand such that the antenna faces the material's center during data acquisition, in order to maximize signal strength and ensure that measurements capture the material's ' overall properties. Meanwhile, convex lens with 15-om-focal-lennth and ~22 m sensing range is used to collimate the beam. With this config- uration, the system achieves beam footprint of 1-5 cm, representing the width of the beam on the material surface. In RF sensing, a smaller beam footprint produces a more focused beam with higher energy concentration, enabling more precise measurement local material properties and yielding richer, more discriminative representations for learning rearning-based models. thereby supporting fine-grained material classification. The lens ensures that the beam footprint is sufficiently small to distinguish materials of varying-sized objects under typical indoor conditions, remains robust to changes in sensing distance and background materials, and provides a consistent sensing region across samples, thereby mitigating potential biases arising from differences in material plate sizes. Appendix D.I visualizes effective beam footprint on material plates.
DATASET
Fine- -nn-ggraied material identification challenging due subtle differences in subcategory materials, sensitivity of RF responses to geometric perturbations, and variability in the physical information captured across frequency bands. Therefore, rich data diversity terms of fine-grained material categories, geometric perturbation simulation, and broad frequency band coverage is crucial for de- veloping and evaluating algorithms applicable to indoor embodied AI scenarios. Thus, we introduce RF-MatlD, the first large-scale, wide-hand, and geometry-divers cation, containing 142k samples evenly split across dual-domain representations, with 71k in the frequency domain and 71k in the time domain. It provides 16 fine fineinnained subcategories, organized into five commonly encountered superclasses in indoor scenarios. To simulate the geometric pertur- bation from real-world data collection, samples in RF-MadI of 200 mm to 2000 mm (at 50 mm intervals) and an angle range of 02 to 10" (at 1 intervals). Each sample spans a wide spectral band of 4 43.5 GHz, uniformly represented by 2,048 bins.
RF dataset for material identifi-
acquired across a distance range
4.1 CATEGORIES OF MATERIALS
RF-MaiID encompasses 16 fine-grained material categories organized into five superclasses. These superclasses represent the most common materials in indoor embodied Al scenarios: (i) bricks, (ii) glass, (iii) synthetic materials, (iv) woods, (v) stones. Within each superclass, select multiple variants that exhibit subtle physical differences, enabling a rigorous evaluation of learning- based approaches challenging fine-grained material classification. Specifically, the fine-grained mate-
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rial categories include: for bricks, (a) overfired clay brick, (b) lightweight perforated brick, (c) lava brick; for glass, (d) transparent acrylic glass, (e) tempered glass, (f) white opaque acrylic glass; for synthetic materials, (g) melamine-faced chipboard, (h) mineral fiber board, (i) solid polyvinyl chlo- ride sheet; for woods, (j) cedar sleeper, (k) luan plywood, (l) red oak plywood: and for stones, (m) permeable paving Stone, (n) agglomerated stone, (o) granite, (p) concrete. Figure presents pictures of the 16 fine-grained materials, annotated with their corresponding tag identifiers for reference. The detailed material sample descriptions are provided in the appendix.
Brcc
10
2011
Figure 2: The visual illustration of the 16 fine-grained material categories.
4.2 DATA COLLECTION AND PREPROCESSING
Realistic Data Collection Motivated by the geometric perturbations typically encountered in data acquisition, we introduce controlled variations both distance and incidence angle, as illustrated in Figure ((b). The Friis transmission equation(Friis, 1946) indicates that, for fixed transmit power Pt. antenna gains G2 and G,, and wavelength X, the received signal power P, decreases as the ef- fective propagation path length (r 2d) increases. Furthermore, for generally rough surfaces, the backscattered signal strength diminishes as the incidence angle 0; increases relative to the incident wave, due to the cosine-dependent reduction in the backscattering coefficient oG, described by the small perturbation method (El-Shenawee & Miller, 2004). In embodied AI applications, these geometric variations reflect realistic conditions where compact
mave sensors deployed on manipulators encounter varying hand-object distances and changing incidence angles. Such varia- tions introduce systematic changes in the RF signal representations defined in equation requiring models to learn invariance and generalize across these sensing conditions.
P, = P.P.,,G,(A//ATTT)
(2)
0, == la W (2kkssin0)). or cos*0;
Concretely, for each material, samples are systematically collected across various distances and an- gles configurations. Data acquisition begins at a distance of 200 mm, with an angle 0%, where 20 samples are recorded. Each sample consists of 2048 frequency-domain bins covering the 4-3..5 GHz band. The angle then incremented from 1 to 10 with 10 samples collected at each angle. After completing all angles the current distance, the distance is increased in 50 mm steps up to 2000 mm, and the same angular sampling procedure is repeated at each step. The process ensures dense coverage of the distance-angle space while increasing the sampling number normal inci- dence. Appendix Figure & 7 show the effect of distance and incidence angle on the frequency signal. Other forms of realistic perturbations are also discussed in the appendix B.4.
Domain Transformation To provide a dual-domain representation. each frequency rrequency-domain domain signal is paired with time-domain signal via inverse fast Fourier transform, capturing complementary spectral and temporal features of the material response. Concretely, the 2048 (N) frequency bins spanning from GHz (fstart) to 43.5 GHz (fend) with uniform spacing A = (fend fstart)/ (N 10240 (Ne)-length time-domain signal. Prior transformation.
1) 19.3 MHz are converted the frequency spectrum is multiplied by a band-pass filter and -added with / A leading zeros ensure correct frequency alignment. The time resolution is Al 1/(N,Af), and the time axis is computed as = c with e = 3 x 10 m/s. Both the trequency-domat and time-domain samples are saved using comma- separated values data format, i.e., " ".csv" files.
Frequency-Domaiee Representation For frequency- domain data, key question arises: should each frequency bin be treated as a single complex element, or should its real and imaginary parts be represented two separate channels, i.e., 2D (length 2) real-valued vector? To address this,

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train two models: deep complex network (Trabelsi et 2017) designed for complex- valued inputs and a Bi-LSTTM (Huang et al., 2015) with input dimension two for the dual-chamnel representation. Our experiments show that the dual-channel representation achieves higher classification accuracy, providing empirical evidence that phase information embedded the real and imaginary parts can be effectively leveraged by learning- -aaeed frameworks.
Post-Pro Tust-Processing To improve the stability and efficiency of model learning, time-domaie data is standardized to have zero mean and unit variance. For the frequency-domain data dual-channel representation, separately normalizing the real and imaginary parts would destroy the correlation (phase) information between channels. Therefore, we perform complex whitening on the com- bined frequency-domain data. The data is centered by subtracting its mean. A whitening trans- form (Koivunen Kostinski, 1999) is then applied by multiplying with the inverse square root of the covariance matrix, obtained via eigen-decomposition. The resulting signal has zero mean, unit variance, and an identity covariance matrix, ensuring that the real and imaginary components are scaled uniformly while preserving the phase information, with its magnitude normalized.
4.3 INTENDED USES
RF-MatID is designed to facilitate broad spectrum of research and applications, especially in the field of embodied AI. As the first publicly available RF material identification dataset and benchmark with diverse protocols and settings, it enables fair comparisons and accelerates the advancement of material identification algorithms. By providing standardized dataset aligned with calibrated radar systems, RF-MatlD enables the evaluation of models' transferability to real-world application sce- narios. For instance, models can be trained on the 24 GHz sub-band in RF-MatID and then evaluated on radar signals captured by embodied agents operating in practical indoor scenarios, enabling sessment of their zero-shot or few-shot transferability. RF-MatlD further facilitates research on do- main adaptation and generalization through cross-domain evaluation settings, advancing algorithm robustness under diverse domains and environmental conditions. From a modality perspective, RF- MatID introduces compact, low-cost sensing platform operating in the UWB-mm Wave spectrum. The platform is specifically customized for indoor embodied Al applications. as it can be integrated onto a robot's end-effector to enable fine- fine-grained local material characterization. This capability supports material aware manipulation and affordance driven workflows, such as selecting grasp strategies according to material compliance, adjusting contact forces based on surface hardness, and enabling downstream policies that rely material grounded affordance cues, for example gras- pable, cuttable, or pourable objects. Moreover, the sensing platform can serve as a complementary modality within multimodal learning frameworks, providing materials' electromagnetic character- istics that enrich the information available for embodied perception (Chen & Yang, 2025; An et al., 2026). For example, when combined with vision-base perception pipelines in indoor scene under- standing, the UWB-mm Wave signals provide fine grained local material characteristics that can be further mapped to material grounded affordances of individual objects. When fused with visual cues that capture spatial structure and object geometry in cluttered environments, this enables embodied agents to achieve more comprehensive scene understanding across multiple dimensions, ultimately supporting more reliable reasoning and action in complex indoor settings.
5 BENCHMARK AND EVALUATION
In this section, we present the key benchmark configurations, the evaluation metrics, the selected deep learning models, and the baseline model design for material identification based on our pro- posed RF-MailD Dataset. We further evaluate experimental results to highlight the limitations of each benchmark model and demonstrate the applicability of learning- based approaches for RF-based material identification across diverse real-world scenarios.
5.1 BENCHMARK SETUP
Frequency Protocol We define five frequency- protocols for RF-based material identifica- tion, capturing both physical distinctions (centimeter vs. millimeter waves) and region-specific regu- latory constraints. Protocol (P1) spans the full spectrum from 4-43.5 GHz. Protocol 2 (P2) focuses on mile analysis, covering 30-43.5 GHz, while Protocol 3 (P3) targets centimeter-wave
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MLP
ResNet- 50
BiLSIM
Trans- former
TimesNet
LSTM- ResNet
CouNNeXi DINOY3
Material- ID
Airfac
Baseline
Protocol 1 (4.0-43.5 (iHa)
$1
99.19
98.85
86.33
91.76
99.66
99.84
99.51
99.28
96.81
99.77
99.57
mod|
84.47
97.17
80.61
84,66
82.87
97.12
81.70
79.10
95.67
91.36
86.62
S2
mud2
73.49
48.90
50,76
40.49
68.58
49.95
65.74
64.19
71.59
86.95
69.47
mod!
76.46
83.16
69.85
79.16
59.93
71.00
66.07
63.52
72.37
65.41
74.09
medl
99.08
99.16
86.78
87.13
98.59
99.69
99.45
98.85
97.63
98.12
98.89
S3
mod2
90.59
59.48
77.05
$3.38
78.99
78.13
85.90
86,27
51.80
76.60
85.23
mod1
96.39
90.42
86.50
82.87
75.50
80.37
94.84
95.21
70,54
75.23
94.21
Protocol, 2 (30.0-43.5 GHz)
S1
98.11
99.82
87,26
92.61
94,78
99.80
99.34
98.56
93.53
91.13
99.47
modl
88.35
95.86
83.75
89,35
84.14
96.82
87, 29
85.13
93.20
91.16
86.87
$2
mod2
69.19
58.82
53.31
56.80
57.96
50.76
68.90
62.65
72.43
71.91
62.31
mod?
79.27
82.12
77.52
79.64
75.15
77.02
81.28
66.81
75.36
66.89
79.81
modl
96.64
95.88
83,96
88.61
93.66
99.36
98.03
95.71
86,86
88.51
97.69
S3
mod2
84.41
51.31
61.45
57.44
67.17
65.15
75.90
73.31
53.09
69.61
75.17
mod3
93.75
94.78
85,70
88,34
87,20
97.59
95.44
94.15
78:49
81.45
96.30
Protocol 3 (4.0-30.0 GIIl))
S1
99.52
99.82
89.87
88.22
99.64
99.78
99.35
98.53
97.32
98.76
99.47
modl
81.57
95.01
78.59
84.22
78.22
95.92
79.45
67.65
97.55
88.84
78.
$2
mod2
62.81
38.45
52.25
42.16
64,58
55.18
65.41
65,28
62.83
73.84
60.07
mod!
65.93
65.29
62.14
76.97
59.50
64.34
53.19
61.68
58.59
54,64
62.63
mod1
99.33
99.31
89.48
90.14
98.56
99.51
99.30
98.30
98.09
95.51
99.22
S3
mod2
89.79
79.20
76.28
62.04
86,26
78.46
90.28
90.63
82.40
87.11
87.17
mod1
95,32
81.63
87.29
74.63
72.29
79.34
93.50
93,23
65.75
62.77
93.47
Number of Model Parameters (M)
67.20
15.98
0.53
0.20
1.34
4.20
28.06
28.05
0.94
1.14
16.27
Table 2: Comprehensive benchmark of deep learning model end-to-end material classification per- formance on RF-MatlD dataset. Accuracy is evaluated and shown in percentage (%) values. Bold indicates the best performance, while underlined values denote the second- and third-best results.
analysis, spanning 4-30 GHz. Pioneeringly, we also consider the practical feasibility of RF-based material identification under legal frequency regulations in major global economies. Protocol 4 (P4) covers frequency bands permitted for commercial RF sensor development in the United States, and Protocol 5 (P5) covers legally allowed bands in China. The details are listed the appendix 4.
Data Splits To evaluate the robustness of learning- based approaches under diverse real-world con- ditions, we define three primary data split settings with seven sub-modes. Setting 1 (S1: Random Split) randomly partitions all RF signal samples into training and testing sets a 7:3 ratio. Setting distribu-
(S2: Cross-Distance Split) partitions the dataset by distance to simulate distance-done tion shifts. Three sub-modes are defined: (i) $2-1, where 11 uniformly spaced distances (out of 37) are used as the test set; (ii) S2-2, where the 11 closest distances are used for testing: and (iii) S2-3, where the 11 farthest distances are used for testing. Setting (S3: Cross-Angle Split) partitions the dataset by incidence angle to simulate angle-domain distribution shifts, also with three sub-modes: (i) S3-1, where uniformly spaced angles (out of 11) are used as the test set; (ii) S3-2, where the smallest angles used for testing; and (iii) S3-3, where the 3 largest angles used for testing. In all of the benchmark results tables, we use the term "mod" as an abbreviation for data split modes.
Category Division To meet various application requirements, we evaluate model performance under three category divisions. The fine- tie-gnaieed division treats all 16 materials as independent classes, regardless of their higher-level grouping. The superclass division groups all subclasses under each superclass into single category (e.g., cedar sleeper, luan plywood, and red oak plywood are unified as "wood"). The subclass division assesses model accuracy in constrained, fine- grained tasks; specifically, we focus the four subclasses under the "stone" category for analysis.
Evaluation Metrics In our benchmark, Accuracy is reported in all experimental analyses. To address the limitation that accuracy may be dominated by majority classes, we also report the Macro FI-score alongside accuracy when presenting baseline results under different task configurations. We further include precision and recall, with the detailed results reported in the appendix Table 6.
Benchmark Models and Baseline Design Considering the sequential dependencies across fre- quency bins as well as the spatial features along the frequency and channel dimensions, we bench- mark diverse set of models commonly used in computer vision, natural language processing, time-
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Protocols
Protocol 1
Protocol 2
Protocol 3
Protocol 4
Protocol 5
Settings
Acc
F1
Acc
F1
Acc
F1
Acc
F1
Acc
F1
S1
99.57
99.57
99.47
99.17
99.47
99.
99.53
99.53
99.41
99.
mod1
86.62
86.66
86.87
86.82
78.18
78.21
87.35
87.26
84.82
84.65
Fine-
S2
mod2
69.47
68.46
62.31
62.12
60.07
59.81
06.27
65.89
67.18
66.45
Grained
mod3
74.09
73.07
79.81
79.22
62.63
62.67
78.40
77.46
72.04
70.63
Division
mod1
98.89
98.89
97.69
97.69
99.22
99.22
98.42
98.42
99.37
99.38
S3
mod2
85.23
85.12
75.17
75.10
87.17
86.96
74.94
74.51
85.11
84.87
mod3
94.21
94.16
96.30
96.27
93.47
93.44
96.65
96.63
95.95
95.90
S1
99.82
99.82
99.78
99.78
99.82
99.82
99.85
99.85
99.87
99.87
mod1
91.15
90.85
94.58
94.43
91.15
90.85
93.50
93.30
94.00
93.998
Superelass Division
$2
mod2
87.24
86.58
88.54
87.98
86.05
85.86
84.69
84.09
85.19
84.61
mod3
87.23
87.50
89.54
89.51
86.48
86.46
89.31
89.67
87.34
87.61
mod1
99.71
99.69
99.34
99.30
99.44
99.40
99.18
99.15
99.60
99.68
S3
mod2
91.26
90.73
89.00 $9.08
88. 61
94.29
93.96
$5.80
84.74
91.68
91.09
mod3
98.11
98.10
99.06
99.02
97.94
97.87
98,99
98.96
98.02
98.01
S1
99.61
99.61
99.72
99.72
99.68
99.68
99.66
99.66
99.72
99.72
mod1
98.77
98.77 98.77
96.36
96.36
95.55
95.51
96.48
96.45
98.05
98.05
Subclass
S2
mod2
85.76
85.54
80.57
80.43
82.16
81.90
86.70
86.54
90.40
90.35
Division
mod3 85.81
85.41
85.80
83.54
81.67
81.63
78.75
76.40
84.15
83.59
modl
99.17
99.17
98.81
98.80
99.10
99:10
99.55
99.55
99.05
99.05
S3
mod2
95.63
95.62
88.
88.04
87.97
95.37
95.37
87.57
87.59
95.39
95.36
mod3
98.29
98.29
97.75
97.74
98.87
98.87
99.08
99.08
98.18
98.17
Table 3: Baseline model performance under various category divisions, data split settings, and pro- Accuracy and macro F1 score are evaluated and shown in percentage (%) values.
tocols.
series, and RF-sensing research. These include Multilayer Perceptron (MLP) (Gardner & Dorling, 1998). ResNet-50 (He et al., 2016), Bidirectional LSTM (BiLSTM) (Huang al., 2015), Vanilla Transformer (Vaswani et al., 2017). TimesNet (Wu et al., 2022), Material-ID (Chen et al., 2025), AirTac (Zhang al., 2024). hybrid LSTM-ResNet model (Choi et al., 2018), ConvNeXt (Liu et al., 2022), and the recent DINOv3 (Siméoni et al., 2025). We also introduce a simple yet robust baseline model that leverages frequency-aware positional encoding to preserve global consisteney. Parallel extractors independently capture spatial and temporal features, which are then integrated into class probabilities via an MIP fusion module. The baseline model achieves an average accu- racy of 85% in all experimental configurations, while the other models perform at approximately 80%. Detailed model implementations are provided in the appendix C.4.
5.2 RESULTS AND ANALYTICS
Domain Comparison Table 4 summarizes material identification results on time- and frequency- domain signals. Under Protocol for fine- fine-grained classification, L.STM-ResNet achieves compara- ble performance on time-domain data to the baseline model on riqqeency-domail data. Given the additional effort to convert frequency signals into the time domain, and significantly higher compu- tational complexity when using 10,240-length time-domain data, we conclude that directly learning from raw frequency-done domain data is more optimized and efficient.
To benchmark model performance and highlight their strengths and weaknesses in material identification, we evaluate all split settings under P1-3 using the fine-graines division, as shown in Table 2. The MLP shows consistently strong performance across configura- tions, but it has the largest parameter size and requires careful redesign of intermediate embedding dimensions to adapt to varying protocols. ResNet-50 and the LSTM- excel under mild do- main shifts (SI, 1, S3-1) but degrade sharply under severe shifts (S2-2/3, S3-3). SOTA vision- based models such as DINOv3 and ConvNeXt perform competitively in challenging scenarios like S3-3. However, the low resolution of the RF data hampers the stability of model convergence, lead- ing to suboptimal results in simpler settings. Sequence-oriented models (Transformer, SILSTM, TimesNet) underperform in most settings and are vulnerable to domain shifts. RF-sensing models generally perform well, but still exhibit notable performance drops under certain nut-of-distributio conditions. The baseline model demonstrates robustness and competitive performance in most cases.
Benchmark Across Models
Quantitative Baseline Results By evaluating the baseline model across all three category divi- sions, seven split settings, and five protocols, we draw several insights from Table 3. RF-based

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100
Time-Domaia
Freq-Domain
Acc
F1
Acc
F1
96.95
96.95
99.57
99.57
modl
85.66 65.64
85.29
86,62
86.66
71.61
69.60
69.47
mod3
74.97
74.32
74.09
73.07
99.65
99.65
S3
med2
94.43
94,42
85.23
85.12
93.70
93.60
94.16
Froquency (GHz)
Comparison between ma- Figure 3: Preliminary experiments consecutive sub-bands terial identification performance on with various bandwidths. Accuracy in percentage value is time- and frequency-domain signals. applied for performance evaluation.
Table 4:
approaches achieve an average material identification accuracy of 96.8%% under S1 across all proto- cols and divisions. However, performance is significantly impacted by domain shifts, with distance shifts causing an average 24. 17% drop and angle shifts a 1 11.41% % drop. We further observe that millimeter-wave signals (P2) are more robust distance variations, whereas centimeter wave sig- nals (P3) better tolerate changes in incidence angle. Interestingly, country-specific legal bands (P4 and P5) achieve performance comparable to the full spectrum (P1), demonstrating the feasibility of RF-based material identification under legal constraints without significant accuracy degradation.
Experiments on Consecutive Sub-bands with Various Bandwidths Band analysis offers valu- able guidance for selecting optimal operating frequencies and designing tailored algorithms for spe- cific applications. In this work, we present a preliminary exploration: as shown in Figure 3, RF signals across different bandwidths generally achieve high accuracy (> 95%) on S1. However, no- ticeable drops appear at certain ranges (c.g., around 10 GHz and in the higher band 35-43.5 GHz), offering useful insights for selecting effective frequency ranges in RF-based material identification.
Discussions By analyzing the benchmark results, we highlight several challenges of learning- based material identification approaches and discuss possible solutions. Training with a standard classification loss captures only data-driven correlations, neglecting physically consistent features, which leads to instability under domain shifts and unconstrained intermediate features. Incorpo- rating physical constraints (e.g.. via PINNs) guides meaningful feature learning, improving inter- pretability and robustness. Additionally, standard training often overfits the source domain, limiting generalization. Domain adaptation and generalization techniques address this by aligning features or adapting parameters, enhancing cross-domain transfer and overall robustness.
6 LIMITATIONS AND CONCLUSION
For future improvements, we identify the following limitations in the RF-MatlD dataset: First, the dataset could be expanded to include richer material variability; it does not yet cover complexi- ties such as multi-layer composite materials or significant thickness-inducee signal shifts. Second, the dataset lacks diverse environmental contexts. Future work should incorporate more complex settings, such as cluttered backgrounds or large open spaces, to account for additional multipath effects and sampled across the broad frequency spectrum, and the raw complex-valued signals have low feature dimensionality. To address these issues the next-generation dataset, we will include variables like material thickness and area to diversify samples, collect real-world data from outdoor production and construction scenarios, increase the sampling rate in application-relevan frequency bands, and leverage classical signal processing techniques to expand and enrich signal feature dimensions.
biases found in real-world scenarios. Third, the RF data is sparsely
In this paper, we present the first open-source, large-scale, wide-band, and geometry-diverss RF dataset for fine-grained material identification, covering 16 fine-grat -nniinnd categories across the 43.5 GHz band with controlled variations in angle and distance, and providing both time- and frequency- domain representations. We further show that raw frequency-domain signals can be effectively leveraged by deep learning models without additional domain transformations, and we evaluate their applicability under versatile protocols. Finally, by benchmarking state-of-the-art models and systematically assessing their robustness to out-of-distribution shifts, our work provides critical foundation for developing more reliable and physically grounded RF sensing systems.
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APPENDIX
The appendix is organized as follows:
Section describes the precise role of the LLMs in this research work.
Section B provides the dataset statistics B.I, detailed frequency allocation protocols in B.2, descriptions of material samples in B.3, discussion on other realistic perturbations in B.4, and Al-oriented intuitions in B.5.
Section outlines the baseline model design in C.I, discusses model-level improvements in C.2, reports complete baseline results across all metrics in C.3, details the implemen- tation of all benchmarking learning-bas -ased models in C.4, and benchmarks classical RF methods in C.S.
Section D visualizes RF data where D.I shows the size of beam footprint at difference distances, and D.2 provides visualizations of frequency- and time-domain data samples across different distances in Figure 6 and different angles in Figure 7.
A THE USE OF LARGE LANGUAGE MODELS (LLMs)
LLMss are used to aid and polish the writing of this paper. Specifically, they assist in refining gram- mar, improving clarity, and enhancing the readability the text. No LLMs are used for the retrieval and discovery of related work, nor for research ideation.
B DATASET DETAILS
B. DATASET STATISTICS
Table 5 presents detailed statistics of the RF-MatID dataset at various levels. Overall, RF-MatID is well balanced, containing 71,040 samples in both the frequency and time domains. At the material- category level, each fine-grained category includes 8,880 samples, while each superclass contains At the distance level, 3,840 samples are collected for each distance from 200 mm to 2000 mm with a step of 50 mm. At the incidence-ame anglle level, 23,680 samples are acquired 0%, and 11,840 samples each the remaining angles from 1 to 10%.
Total number samples
Frequency-domain 71,040
Time-domain: 71,040
Samples per Material Category
Bricks ((a), (b), (c)]; 8,880
Glass [(d), (e), (f)): 8,880 *
Synthetic Materials ((g). (h), (i)): 8,880 * 3
Woods [(j), (k), (I)]: 8,880 *
Stones [mm), (n), (o), (p)): 8,880
Samples per Distance (d)
11 = 3, 840, d E (200, 250, 2000)mm
Samples per Incidence Angle (8)
78=00 = 23, 680
n2 = 11, 840, (1",2,.... 10
Table 5: Detailed RF-MailD dataset statistics.
B.2 DETAILS OF REGIONAL LEGAL FREQUENCY BAND
Protocol 4 and Protocol are defined in accordance with the global passive service protection re- quirements stipulated by the ITU Radio Regulations (RR. 2024 Edition) (ITU, 2024). According to provisions such as RR 5.340, RR 5,482, RR 5.511A, RR 5.547, and RR 5.551H, certain bands between 44 GHz are reserved exclusively for passive services, including Radio Astronomy, Earth
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Exploration-Satee Service (EESS) passive, and meteorological sensing. These bands strictly protected from active emissions worldwide, and include: 10.6-10.7; 15.35-15.44 15.40; 23.6-22.2000 31.3- 31.8; 36.43-36.... 42.5-43.5 (GHz).
Protocol selects its frequency bands based on the requirements of the U.S. Federal Regulations (Office of the Federal Register, National Archives and Records Administration, 2024) and the pro- tection provisions specified by the ITU Radio Regulations. Protocol follows the Radio Frequency Allocation Regulations of the People's Republic of China (2023 Edition) (Ministry of Industry and Information Technology of the People's Republic of China (MIIT, 2023) issued by MIT,, excluding bands designated for amateur use and the passive protection bands defined by the ITU(TU,, 2024). The legal frequency bands of Protocol 4 and Protocol 5 are shown in Figure 4. Valid bands are obtained by filtering out non-compliant frequencies and concatenating the remaining segments. The numerical information of these valid ranges is then paired with their corresponding data to construct the training input.
P2: Millimeter Frequracy Hand
Cratimater Fraquacry Band
10
15
Legal Frequency
Logal Frequency
Figure 4: The visual band allocation of each frequency protocol. The frequency values are in GHz.
B.3 DETAILED MATERIAL DESCRIPTIONS
(a) Overfired Clay Brick Rectangular, 210 x 100 60 mm, approx. 2.25 kg. made from high-iron earthenware fired above its optimal temperature. The intense firing produces a dense microstructure (~ 1.786g/cm) //88g//mm)) with a hard, vitrified surface (~~40 40 um roughness), deep red-brown body, and dispersed white grog inclusions. Its low porosity and high abrasion resistance make it suitable for high-strength masonry, paving, or other applications where durability and a rustic, speckled appearance desired.
Lightweight Perforated Brick weigh,,, thin-format perforated clay brick, 210 100 30 mm, extruded from fine red earthenware and fired to achieve uniform reddish-brown body (&s 1.667g/cm²) with smooth, low-porosity surface (~ 18 pm roughness). Longitudinal perfora- tions reduce mass and thermal conductivity while maintaining dimensional accuracy and sufficient compressive strength for veneer facings, partition walls, and infill panels. Its slim profile enables rapid coursing and reduces dead load, making it well-suited for modern energy-efficier masonry and interior cladding systems.
(c) Lava Brick Rectangular, 200 x 100 25 mm, 1.08 kg, sawn from dense vesicular basalt (p x 2.16g/cm²). Charcoal grey with a fine "salt-and-pepper speckle of plagioclase crystals (~ 180 μam roughness); minute sealed vesicles reduce weight while retaining strength. Fire-resistant, low-porosity, and highly reesstann,,,,,, it is ideal for paving, facade cladding, or heat-storage applications.
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The clear acrylie (PMMA) strip has 300 x 210 x 5 mm size, 0.33 kg weights, ~ 1.048 g/cm² density. It is optically transparent, lightweight, and impact-resistant, providing glossy, gikes-like appearance without the brittleness of conventional glass.
(d) Transparent Acrylic Glass
(e) Tempered Glass Rectangular tempered glass pane, 300 x 200 x 5 mm, ~ 0.72 kg. ~ 2.4 g/ cm² density, water-clear with the characteristic green edge tint of soda-lime float glass. fleat-oogghnnng induces surface compressive stress, making it 3-5 x stronger and significantly more impact- and thermal-shock-rrsistatn than ordinary annealed glass. When broken, shatters into small, blunt "dice" fragments, making it suitable for shelves, tabletops, appliance doors, and other upplications requiring high strength and safety glazing.
(f) White Opaque Acrylic Glass Solid opaque white Plexiglass, 300 x 202 x 10 mm size, 0.55 kg weights, 0.908 g/ 3/cm² density. lightweight and more shatter-resistant than glass. Com- monly used for signage, light diffusers, photography backgrounds, shelving, and various DIY or craft projects.
(g) Melamine-faced Chipboard A composite material, 600 x 450 x 4 mm size, 0.87 kg weights, 0.806 g/cm² density, consisting of a particleboard core-wood chips, shavings, and sawdust bonded with esinn-covered by thin, decorative layer of melamine another plastic laminate. Commonly used in flat-pack furniture, including bookshelves, cabinets, and desks, for its smooth surface, durability, and ease of cleaning.
(h) Mineral Fiber Board Made from a blend of mineral fibers (such as recycled slag, stone, or fiberglass), 910 x 450 10 mm size, 0.25 kg weights, 0.061g/cm //m/ density, fillers like perlite or clay, and a binder, typically starch. Primarily used in suspended or "drop" ceilings in commercial spaces, such offices, schools, and retail stores, improve acousties by reducing echo and noise.
(i) Solid Polyvinyl Chloride Sheet A solid sheet formed from a single thermoplastic polymer (PVC) via extrusion or casting. 600 x 300 9 mm size, 0.46 kg weights, 0.284g/ cm3 density. is versatile material employed in signage, display boards, wall cladding, chemical-resistant work surfaces, and for fabricating custom parts or enclosures.
(i) Cedar Sleeper Rectangular cedar sleeper, 450 x 190 x 95 mm, approx. 3.6 kg (bulk s 0.443g/ )m)),, sawn from knot-free heartwood with closely spaced annual rings visible on the end grain. The dark reddish-brown timber contains natural thujaplicins and other extractives that provide high durability, insect resistance, and a pleasant aromatic scent. Radial and tangential surface checks typical as the low-density wood seasons. Ideal for landscaping borders, raised beds, outdoor furniture where light weight, decay resistance, and a rustic, rough-sawn aesthetic are desired.
(k) Luan Plywood An engineered wood panel made from thin layers of wood veneer glued to- gether. 600 x 300 x 12 mm size, 0.75 kg weights, ~ 0.347g/ cmm density. Luan is commonly used for general-purpose plywood due its smooth surface, light weight, and affordability. It popular for cabinetry, interior paneling, furniture backing, and various DIY projects.
() Red Oak Plywood Made from Red Oak, a widely used and recognizable North American hardwood. 600 x 300 x 5 mm size, 0.49 kg weights, ~ 0.544g/cm/ cm3 density. Its strength, durability, and attractive grain make Red Oak plywood a staple for cabinetry, furniture, flooring, and decorative interior paneling.
(m) Permeable Paving Stone Square permeable paver, 300 300 35 mm, approx. 3.8 kg. made by sintering angular volcanic aggregate into open-graded matrix (bulk p R 1.206g/ ccm²). Dark charcoal-grey with uniformly exposed 2-5 mm basalt chips, its interconnected void network enables rapid vertical drainage while maintaining high compressive strength and freeze-thaw dura- bility. Ideal for Normwater-friendll walkways, plazas, and green-infrastructure paving that require load-bearing capacity, slip resistance, and rugged, monolithic volcanie appearance.
(n) Agglomerated Stone A composite stone formed by binding fragments (clasts) of various rocks and minerals with cementitious resin-based binder. 300 x 300 30 mm size, 6.25 kg weights,
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2.315g/ cm3 density. The result is durable, uniform material that mimic natural stone for flooring, countertops, and cladding.
(o) Granite A classic "salt-and- salt-and-pepper" igneous rock composed of interlocking crystals of light- colored minerals (white to gray quartz and feldspar) and dark-colored minerals (black biotite mica or hornblende). 300 x 300 x 30 mm size, 7 kg weights, ~ 2.593g / /em& density. Its granular texture and high hardness make extremely durable, widely used for countertops, flooring, building facades, and monuments.
(p) Concrete A man-made material without the varied crystals or patterns of natural stone. The small surface pinholes and voids are trapped air bubbles from the casting process. 300 x 300 50 mm size, 10.65 kg weights, ~ 2.367 g/ em3 density. valued for its versatility, compressive strength, and adaptability in construction and paving applications.
B.4 DISCUSSION OF OTHER REALISTIC PERTURBATIONS
Beyond systematically incorporating geometric variations that reflect typical perturbations encoun- tered in indoor embodied AI scenarios, RF-MatID also inherently captures aspects of real-world RF conditions, including material variability and multipath reflections. Material variability (e.g. in density, roughness, and dielectric properties) is inherently reflected in the dataset. For instance, lightweight perforated bricks and lava bricks produce distinct signatures the raw frequency- domain signals due materials' density and surface roughness differences. Multipath effeets also naturally present, as time-domain signals visualized in Appendix Figure 7 showing secondary reflections beyond the direct path.
RF-MatID intentionally does not include explicit perturbations from environmental variability, me- chanical vibrations, electromagnetic (EM) interference based on the following considerations. Environmental factors, such humidity, are relatively controlled in typical indoor embodied AI scenarios and are expected to have negligible impact on RF signal propagation and material char- acterization. Mechanical vibrations are typically compensated by the robot's control algorithms. EM interference has minimal effect on our FMCW radar measurements in indoor settings. The continuous linear frequency modulation of FMCW signals allows echo separation even under multi- target or overlapping frequency conditions... Moreover, commercial off-the-shelf RF devices overlap with sensor's s operating bands only in narrow frequency segments (c.g., 0.8 GHz of 5-5.8 GHz WiFi and 1.5 GHz segments of UWB bands), and our sub-band analysis demonstrates that material classification can be reliably performed outside these ranges.
B.5 DISCUSSION OF MECHANICAL GROUNDED AI-ORIENTATION INTUITIONS
We will discuss the following Al-oriented intuitions based on mechanical material-specific details that worth future explorations.
Standard deep learning backbones operate in the real domain, this presents a critical architectural choice: whether to employ specialized Complex-Valle Valued Neural Networks (CVNNs) or to project the data into a dual-channel real-valued representation. Our ex- periments in Section 4.2 demonstrate that the dual-channel approach is superíor. allows standard models to effectively learn the latent interactions between amplitude and phase, yielding improved out-of-distribution (OOD) generalization and more accurate fine- nne-grained classification.
Complex Signal Representations:
Embedded Physical Constraints: In physical science, radar equations and other physical laws well studied. Incorporate them as regularization terms or hard constraints in the model could guide feature learning according to known material electromagnetic responses.
Disentangled Representation Learning: In material science, intrinsic material properties (e.g., per- mittivity, density) be separated from geometric factors (e.g., distance, incidence angle). Incor- porating disentangled representation learning could guide the model to capture geometry-invane invariant material features while representing geometric variations linearly.
Spectral Attention: In RF sensing, materials are identified by unique characteristics occurring at certain frequencies (e.g., periodic fluctuations sharp energy changes) that reflect their thickness
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and internal structure rather than overall signal strength. Trequency-domaii attention guide models to focus on the most informative frequency spectrum for material discrimination.
C BENCHMARK DETAILS
C.I BASELINE MODEL
To address the instability of models specializing for spatial or temporal features under complex ex- perimental configurations, and frequency-domaie data under realistic protocols is composed of mul- tiple non-contiguous slices, we design a simple yet robust baseline model. A Trequency-awant po- sitional encoding is introduced to preserve global consistency across fragmented frequency-domain data, ensuring that an identical frequency position yields an identical embedding. To independently extract spatial and temporal features, dedicated extractors are utilized in parallel manner. Specif- ically, we utilize a 3-stage ConvNext model as a spatial feature extractor and a 3-encoder-layer Transformer structure as temporal feature extractor. Finally, a fusion module is applied in- tegrate features into class probability distributions. We experiment with both attention-based and fully connected fusion mechanisms, and empirically adopt an MLP-based fusion design due to its effectiveness.
C.2 MODEL-LEVEL DISCUSSIONS
By analysing the benchmark results, we highlight several challenges of learning- based material iden- tification approaches and discuss possible solutions. Firstly, training solely with standard classi- fication loss captures only data-driven correlations rather than physically consistent discriminative features, leading to instability under domain shifts and offering no constraints on intermediate fea- ture distributions. Incorporating physical equations or consistency constraints (e.g., via PINNs) can guide the model learn meaningful features, improving interpretability and robustness. Secondly, models trained under standard procedures also tend to overfit the source domain, limiting general- mitigate ization unseen domains. Domain adaptation and domain generalization techniques this by aligning feature distributions or adapting model parameters, thereby enhancing cross-domain transfer and overall robustness. Thirdly, existing baseline models jointly train spatial and sequential feature extractors, limiting feature expressiveness, which can be mitigated by incorporating pre- trained modules that capture more distinctive representations. Fourthly, directly concatenating spa- tial and temporal features and fusing them with an MLP can lead to misaligned representations and fails to explicitly model the physical relationships between space and time, which can alleviated by introducing feature alignment and designing a physics-inspired fusion module.
C.3 ALL. CLASSIFICATION METRICS EVALUATED IN EXPERIMENTS
We adopt four widely used metrics to evaluate model performance on the material identification task. Accuracy, the most common metric, is reported in all experimental analyses. Precision defined as the ratio of correctly identified positives to all predicted positives, while Recall the ratio of correctly identified positives to all actual positives. Since precision and recall individually provide only a limited view of model performance, their detailed results are reported in the Table 6. To address the limitation that accuracy may be dominated by majority classes, we also report the Macro FI-score, which computes the F Fl-score for each class independently and then averages across classes.
C.4 BENCHMARK MODELS
LSTM ResNet:
We pair a multi-layer LSTM front-end with a 1D ResNet back-end to capture both long-range dependencies and local motif' in TERA-MATERIAL'S RF sequences. The input is a dual-channel stream (I/Q), so the LSTM ingests (T, 2) and outputs contextualized (T, H) repre- sentation. We then permute to (H, T) and feed it to 1D ResNet (Conv I residual blocks and stagewise nown-sampling), converting the LSTM's hidden size into convolu- tional channels, Unlike the 2D image variant, all kernels are ID to operate along frequency/time. An Avg D makes the model length-agnostic, and lightweight MI.P head (512-2566-6
with
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Fine-Grained Division
S1
S2
S3
S1
S2
S3
mod1
mod2
mod3
med1
mod2
mod3
mod]
mod2
mod3
mod1
mod2
mod3
P1
99.5
86.62
69.47
74.09
98.89
85.23
94.21
99.57
86.66
68.46
73.07
98.89
85.12
94.16
P2
99.47
86.87
62.31
79.81
97.69
75.17
96.30
86.82
62.12
79.22
97.69
75.10
96.27
P3
99.47
78.18
60.07
62.63
99,22
87.17
93.47
99.47
78.21
59.81
62.67
99.22
86.96
93.44
P4
99.
53
87.35
66.27
78 40
98.42
74.94
96.65
99.53
87.26
65.89
77.46
98.42
74.51
96.63
P5
99.41
84.82
67.18
72.04
99.37
85.11
95.95
99.41
84.65
66.45
70.63
99.38
84.87
95.90
P1 99.57
86.62
69,47
74.09
98.89
85.23
94.21
99.58
87.00
69.99
77.76
98,92
85,52
94.68
P2
99.47
86.87
62.31
79.81
97.69
75.17
96.30
99.48
87.23
63.81
81.52
97.72
76.16
96.61
P3
99.47
78.18
60.07
62.63
99.22
87.17
93.47
99.47
78.50
64.41
69.01
99.23
87.55
93.95
P4
99.53
87.35
66.27
78.40
98.42
74.94
96.65
99. 04
87.64
66.44
81.56
98.44
76.
96.94
P5
99.42
84.82
67.18
72.04
99.37
85.11
95.95
99.41
85.05
68.26
76.89
99.38
85.71
96.05
Superclass Division
S1
S2
$3
S1
S2
S3
mod1
mod2
mod3
mod1
mod2
mod3
mod1
mod2
mod3
mod1
mod2
mod3
P1
99.82
91.15
87.
87.23
99.71
91.26
98.11
99.82
90.85
86.58
87.50
99.69
90.73
98.10
P2
99.78
94.58
88.54
89.54
99.34
89.08
99.06
99.78
94.43
87.98
89.51
99.30
88.64
99.02
P3
91.15
86.05
86.48 48
99.44
94.29
97.94
99.9 82
90.85
85.86
86.46
99,40
93.96
97.87
P4
99.85
93.50
84.69
89.31
99.18
85.80
98.99
99.85
93.30
84,09
89.67
99.15
84.74
98. .6
P5
99.87
94,00
85, 19
87,34
99.69
91.68
98 02
99.87
93.98
84,61
87.61
99.68
91.09
98.01
P1 99.81
93.46
86.97
87.54
99.69
90.92
98.18
99.83
93366
87.17
90.49
99.70
91.21
98.04
P2
99.77
94.41
88.23
89.53
99.30
88.51
99.00
99.79
94.57
88.60
89.56
99.33
88.88
99.07
P3
99.8 81
90.87
85.83
86.49
99.40
94.09
97.93
99. 82
90.88
86.03
86.56
99.41
94.52
97. 84
P4
99.8
84
93.26
84.06
89.64
99.13
85.14
98.
99.85
93.74
84.65
90.30
99.19
85.26
98. 96
P5
99.86
93.98
85. 17
87. 47
99.67
91.25
98. 10
99.87
93.99
68.68
89.23
99.69
91.68
97.94
Subclass Division
S1
$2
S3
S2
S3
mod1
mod2
mod3
mod1
mod2
mod3
mod]
mod22
mod3
mod1
mod2
mod3
99.61
98.77
85.76
99.17
95.63
98.29
98.77
85.54
85.41
99.17
45.62
98.29
P2
99.72
96.36
80.57
85.80
98.81
88.04
97.75
99.72
96.36
80,43
85.54
98.80
87.97
97.74
P3
99.68
95.55
82, 16
81.67
99.10
95.37
98.87
99.68
95.51
81.90
81.63
99.10
95.37
98.87
P4 99.66
96.48
86.70
78.75
99.55
87.57
99.08
99.66
96.45
86.54
76.40
99.55
87.59
99.08
P5
99.72
98.05
90.40
84.15
99.05
95.39
98.18
99.72
98.05
90.35
83.59
99.05
95.36
98.17
P1 99.
60
98.77
85.76
85.81
99.17
95.62
98.29
99.62
98.78
86.78
87.36
99.17
95.72
98.
P2
99.72
96.36
80,57
85.80
98.81
88.04
97.75
99. 72,
96.39
82.88
86.71
98.83
88.13
97.79
P3
99.68
95.50
82. 16
81.67
99.10
95.37
98.87
99.68
95.79
82.34
81.81
99.10
95.39
98.89
P4
99.66
96.48
86.70
78.75
99.55
87.57
99.08
99.66 66
96.47
88.63
82.06
99.55
87.73
99.11
P5
99.72
98.05
90.40
84.15
99.05
95.39
98.18
99.72
98.06
92.03
85.60
99.06
95.45
98.20
Table 6: Complete baseline results under various category divisions, data split settings, and proto- cols. Accuracy ), macro Fl score percentage (%) values.
), Recall
) and Precision
are evaluated and shown in
classification. This hybrid design targets amplitude/phase order (LSTM) and fine-grained spectral patterns (ResNet), improving robustness to distance/angle perturbations.
performs
ID Transformer: We treat each spectral/time bin as a token and apply a lightweight Transformer encoder tailored to ID RF signals. A linear input projection maps multi-channel inputs (e.g.. I/Q) to an embedding; a fixed sinusoidal positional encoding preserves order over the 2,048 bins. We use PyTorch encoder layers with batch-firs, layout and compact feedforward width (256) plus 0.1 dropout to control capacity and mitigate overfitting under distance/angle perturbations. Instead of CLS token, we adopt global mean pooling across tokens, which is simple, stable, and length- flexible (for any sequence less than or equal to the preset max). A single linear head produces logits. This design captures long-range cross-band interactions without imposing locality biases from convolutions.
R15 adapt the image ResNet-50 to ID RF sequences by replacing 2D kernels with IxI-3x1-1x1 bottlenecks and BatchNormID. Dual-channel I/Q inputs (B, L, 2) are transposed to (B, 2, L) and passed through a 7x1, stride-2 stem with max- mao-pooling, followed by stages [3.4,6,3] of BottleneckID blocks. nown-samplin is applied via stride the 3x1 conv and a projection shorteut when shape/stride changes, preserving residual alignment. An AdaptiveAvgPooll yields length- agnostic features, and 2048 C linear head performs classification. This ID design enforces
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locality along frequency/time, capturing spectral edges and resonances while providing translation invariance and hierarchical abstraction; it is a strong baseline under distance/angle perturbations.
DINOv3 (ConvNeXt-Tiny adapter): We repurpose a DINOv3 Con-NeXt-Tiny
feature extractor for ID RF sequences. The sequence (T. 2) is chunked into non-overlapping patches (length patch_size), each flattened and linearly embedded to 1024 dims, then reshaped to 32x32. These per-patch "images" are stacked along the channel axis (channels = T/patch.size) and fed to the backbone after replacing the first Conv2D to accept this channel count (kerneU/stride/paddiin preserved). Backbone parameters can be frozen to retain DINOv3 priors. We take the backbone output, apply global average pooling (2D token-wise? and train single linear head. This design transfers robust DINOv3 texture/shape priors to RE, while segmenting the sequence into fixed-length patches exposes local spectral patterns and preserves long-range cross-band context through deep receptive fields.
We two-layer bidirectional LSTM (hidden=128 per direction, batch first=True) tailored to dual-channel I/Q inputs shaped (T, 2). Instead of pooling over time, we concatenate the last hidden states from the forward and backward passes, hn[-2], hn[-1], to obtain a compact 256-1) sequence summary that is sequence-lengle agnostic. A bias-free linear head maps this summary to class logits, reducing parameters and regularizing the classifier. Bidirectionality captures cross-band dependencies that can appear in both causal and anti-causal orderings, while the final-state readout emphasizes global context over local noise from distance/angle perturbations. This minimalist de- sign is fast, memory-light, and effective for fine-grained material discrimination.
BiLSTM:
As no-frills baseline, we flatten each (2048. I/Q sequence to a 4,096-D vector and feed it to a two-layer MLP with an expansion ratio of 2 (4096-&8 -8192->4096 and ReLU nonlin- carities. This deliberately discards ordering, testing whether global co-occurrence statistics across the spectrum suffice for discrimination on T'ERA-MATERIAL. The final bias-free linear classifier maps the 4,096-D representation to class logits, slightly regularizing the head. The design creates dense, global cross-band mixing without inductive biases from convolutions/attentioo. providing a parameter-efficieet
MLP:
SPU-friendly baseline. The trade-off is a fixed input length (tied to 2048x2); we note possible extensions (lazy hai/paadding/masking) to support variable lengths while preserving the simplicity of the architecture.
We adapt TimesNet for classification on RF I/Q by mapping (T, 2) into dmodst 32 with a ConvID token embedding (kernel=3, circular padding) plus sinusoidal positional encod- ing-no calendar/time features. Each TimesBlock estimates the top-k periods via rFFT, pads to period multiples, folds the series into (steps x period) grid, and applics two Inception-style 2D conv stacks 132-+128-22 to capture temporal 2D-variation. Outputs from the top-k periods are Neftmax-weighte and added residually. We use k=3, one TimesBlock layer, and LayerNorm. For classification, we apply GELU + dropout, flatten (T- m.ddet) and linear head to C classes. These choices target multi-scale periodicity and fine spectral textures from material resonances while keep- ing parameters modest.
TimesNet:
ConyNeXt: We adapt ConvNeXt to RF I/Q by reformatting 1D sequences into pseudo-images. A preprocessing stage uses Conv 1d (kemel-stride--aathh size) to project each patch of (T, 2) into 1024 dims, adds sinusoidal positional encodings, then reshapes 1024-43xx22. Patches are stacked as channels, yielding (B, Npatch. 32, 32); we set ConvNeXt's in_chans N pattch- When T isn't divisible by patch_size, we pad to the nearest multiple. The backbone is standard ConvNeXt (7x7 depthwise conv, LayerNorm, 4xMLP, DropPath, layer scale; downsampling 4 then 2x, 2x, 2x), followed by global average pooling and linear head. This mapping lets spatial kernels learn intra-patch spectral texture, while pointwise mixing aggregates cross-patch cues building robust, hierarchical features under distance/angle perturbations.
C.5 BENCHMARK ON CLASSICAL RF METHODS
We have surveyed classical RF processio and hybrid approaches specifically proposed for material identification, selected two representative methods, mSense (Wu et al., 2020) and
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RFVibe (Shanbhag et al., 2023), and evaluated them under the same RF-MatID protocols. The results have been incorporated into the table below.
We observe that mSense fails to distinguish the fine- the-grained material categories, primarily because classical methods rely on background-onl, measurements for noise removal, while our dataset trains directly signals that include background noise.
mSense
RFVibe
P2
P3
P1
P2
P3
S1
10.31
8.53
10.05
83.76
86.48
79.06
mod1
9.91
8.47
10.33
80.48
82.51
91.09
S2
mod2
13.10
10.25
10.74
50.36
52.16
50.28
mod3
8.51
7.95
8.20
72.20
82.70
67.93
mod1
10.38
9.74
9.09
83.45
86.50
80.27
S3
mod2
7.50
6.38
10.30
55.97
69.59
76.50
mod3
6.42
6.81
6.42
76.48
92.93
71.28
Table 7: Benchmark of a classical RF signal-processing approach and hybrid method on RF-MatID dataset. Accuracy is evaluated and shown in percentage values.
D VISUALIZATIONS
D.1 BEAM FOOTPRINT VISUALIZATIONS
This subsection presents visualizations of effective beam footprints on material plates, In the figure below, D2 refers the placement distance and the color bar the right side indicates the energy concentration values.
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D2 6. 5cm
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Figure The visualization of beam footprints across various distances
D.2 DATA SAMPLE VISUALIZATIONS
This subsection presents visualizations of both time- and frequency-domain samples. The frequency-domain complex signals are plotted in terms of their real and imaginary components.
22

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Published as a conference paper at ICLR 2026
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0.0075
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Figure 6: The visualization of frequency-domate and time-domain data across various distances
23

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Published as a conference paper at ICLR 2026
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Figure 7: The visualization of frequency-domain and time-domain data across various angles
24


Reviews:
[{"review_id": "GDQMxXakZd", "reviewer": "Reviewer_VGTy", "summary": "This paper presents RF-MatID, a large open-source dataset for radio-frequency material identification. It contains 142,000 samples across 16 categories and 5 superclasses, covering 4–43.5 GHz in both frequency and time domains, with controlled variations in angle and distance. The authors benchmark nine deep learning models under multiple frequency and data split settings, showing that raw frequency-domain data can be effectively used. The main contributions include the creation of this dataset, comprehensive benchmarking protocols, and analysis of model robustness and frequency-band effectiveness.", "questions": "1. The authors claim that \"the system achieves ∼10 cm spatial resolution\". The concept of \"spatial resolution\"is not very clear to me.\n2. I notice in Figure 2 that different material plates have different sizes. Does this setting introduces additional bias into the RF data for each class? For example, a larger plate will make the signal different in some ways?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. First large-scale and open-source RF dataset covering a wide frequency range. The scale and diversity of this dataset can well benifit the community of RF-related machine learning-based study.\n2. This paper establishes a well-structured benchmark with multiple frequency protocols, data splits, and nine deep learning models, offering a comprehensive evaluation of model robustness.\n3. The authors carefully analyzes both frequency and time domain representations. This leads to the insight that frequency-domain data alone can achieve high accuracy, which simplifies model design.\n4. The dataset is well organized, rather than just collected. This facilitates future research in RF sensing and cross-domain generalization.", "weaknesses": "1. The term \"perturbation-aware\" is slightly overclaimed. The authors introduce \"perturbation\" by varying the distance and angle, which is controllable as part of the measuring/sensing technique itself. As the authors mention, the \"real-world interference\" is not well considered, like electromagnetic noise, mechanical vibrations, etc.\n2. One minor suggestion is that some acronyms should be explained before used, like UWB and MMW. Besides, I am not sure whether \"mmWave\" and MMW mean the same concept. If so, it seems inconsistent.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rdTmnHAqfb", "reviewer": "Reviewer_vQa1", "summary": "Focusing on material identification, this work indicates that there lacks a large-scale datasets with real materials labeled by frequency signals. According to this paper, frequency-domain signal data can not only increase the material identification, but also enhance the generalizability to real-world applications where a domain gap always exists. Therefore, this work proposes a frequency-labeled dataset with 142k samples, 16 fine-grained categories.\n\nMoreover, this work integrated a benchmark that evaluates 8 previous works and one proposed baseline. The benchmark tests the domain adaptation ability, reveals some existing challenges.", "questions": "Please address the questions in weaknesses if possible.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. Large dataset and effective data preprocessing: This work collected 71k frequency samples and extend them to time-domain representation, which is a good contribution for real-world applications. In addition, the data preprocessing is carefully designed to augment the data sources.\n2. Comprehensive benchmark: The benchmark evaluated different protocols for different real-world usage. The domain adaptation evaluation is conducted on reasonable data split strategies, and the hierachical label split can effectively reveal the model's capability for material identification.\n3. This work is well motivated and has the potential to inspire domain experts to design and test new framework for material identification.", "weaknesses": "1. Presentation: The presentation involves too many mechanical material-specific details, while, correspondingly, lacks AI-oriented intuitions, which is not friendly for a broader community. For example, how the details of Eq. (1) contribute to this design.\n2. The benchmarked methods are all general methods that originally proposed for other downstream tasks. Is there any material identification specific methods, or frequency process focused methods worth to be includded?\n3. The contributions to the AI community still remain to be discussed. The authors mentioned that the dataset can contribute to multi-modal learning. However, this is a weak claim without further demonstrations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5pI9Ev0ECw", "reviewer": "Reviewer_FgEZ", "summary": "This paper introduces a large-scale dataset and benchmark (RF-MatID) for radio-frequency–based material identification.\nThe dataset spans 16 material types across 5 superclasses.\nIt includes realistic perturbations such as varying angles and distances to test robustness.\nThe authors further establish multiple benchmark protocols to evaluate state-of-the-art models under both standard and distribution-shift conditions.\nThe work aims to standardize evaluation and accelerate progress in RF-based material recognition for real-world applications.", "questions": "See weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This work addresses a current gap in RF-based material sensing by providing a new dataset and benchmark focused on material identification.\n- The dataset is relatively large, covers a wide frequency range, and incorporates variations in acquisition conditions.\n- The dataset includes of both time- and frequency-domain representations, along with several evaluation protocols targeting both in-distribution performance and robustness to angle and distance shifts.\n- The authors also benchmark several state-of-the-art deep learning models across these protocols, establishing initial baselines for comparison and facilitating future reproducible work in this area.", "weaknesses": "- More discussion on the dataset's limitations, potential biases, and cost or practicality of data collection would help contextualize the scope and applicability of the benchmark.\n- While the dataset is extensive, the paper would benefit from further clarification on the real-world representativeness of the collection setup and whether the acquisition hardware and environments generalize beyond the authors' configuration.\n- The evaluation focuses primarily on standard deep learning baselines, and it is unclear how classical RF signal-processing methods or hybrid approaches would perform under the same protocols.\n- Although perturbations in angle and distance are included, other sources of real-world variability are not explored in depth.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VJRr0kMee5", "reviewer": "Reviewer_QnNM", "summary": "The authors introduce a new RF-sensing based benchmark called RF-MatID for material identification. This benchmark is large in its scale across the frequency range from 4 to 43.5 GHz and the number of samples in the time/frequency domains. \nThe dataset contains 5 superclasses (brick, glass, synthetic materials, woods, and stones) and 16 fine-grained material categories for different variants of the superclasses. Each sample is collected at varying distances (200–2000 mm) and incidence angles (0–10°) to simulate real-world perturbations. The authors benchmark nine models popular in vision, language, and time-series domains across multiple settings, protocols, and divisions. The results have high in-domain accuracy around 99% and analyze robustness to cross-angle and cross-distance shifts. Their results show that raw frequency-domain data performs comparably or better than time-domain conversions, and that the dataset supports evaluation under different global frequency regulations.", "questions": "Please see my weakness for most of the concerns. Some questions for authors to discuss are:\n- Can the authors justify the dataset’s relevance to long-range sensing tasks such as autonomous driving or outdoor perception given that the sensing range is very close-distance\n- Since the frequency-domain data can be easily transformed into the time-domain via FFT, what new information does the time-domain dataset provide that cannot be derived from the frequency data?\n- Are the perturbations (distance and angle) annotated per sample, and are these metadata available in the dataset? Are there more textual annotations that we could use to give LLM the capabilities to describe the material?\n- The paper reports high accuracy (>99%) across models, could this indicate potential overfitting due to low intra-class diversity or lack of environmental variability? And given the already high performance on the proposed benchmark, is the task sufficiently challenging to drive future methodological improvement?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The dataset is clearly described and prior limitations are organized well\n- The benchmark provides a good structured taxonomy for fine-grained classification.\n- The benchmark covers comprehensive materials with 16 fine-grained attributes and five superclasses\n- The authors evaluate the benchmark on some popular deep learning models from the vision domain.", "weaknesses": "- The dataset operates at a sensing range of only ~2 m, which is insufficient for real-world applications such as autonomous driving, robotics, or drone perception that typically require >10 m detection. This limitation substantially weakens the claimed motivation of supporting “autonomous systems” and confines the dataset’s applicability to laboratory-scale studies.\n- The introduced perturbations are restricted to geometric variations (distance and angle) within a highly controlled indoor environment. The absence of electromagnetic interference, multipath reflections, or environmental factors (e.g., humidity, surface roughness variability) prevents the dataset from reflecting realistic RF conditions. While the authors acknowledge this limitation, the overall contribution remains narrower than claimed.\n- The inclusion of both frequency- and time-domain data provides limited additional value, as the time-domain samples are directly computed via inverse FFT. This dual-domain representation described in the paper does not really introduce new empirical diversity but rather duplicates information that practitioners could easily derive themselves. The dataset nominally contains 142k samples; however, only 71k represent unique physical measurements. But the dual-domain counting somewhat inflates the dataset’s reported scale, and it is therefore concerning whether the reported dataset size is artificially inflated by redundancy and the contribution is overclaiming.\n- The benchmark focuses solely on material classification. The benchmark, therefore, provides limited insight into the broader applicability of RF sensing for real-world material-based applications.\n- Most of the models evaluated are vision, language, and time-series based. The baseline encodes some frequency information. However, there are many works on RF-sensing models, but they are not evaluated.\n- From Figure 2, many material samples appear visually and structurally similar, suggesting a high intra-class correlation. The dataset lacks evidence of variability in properties such as texture, thickness, or surface roughness, making it unclear whether the reported generalization extends to real-world materials beyond those collected in the lab.\n- **Minor:**\n    - The use of the term “mod” for both *modality* and *mode* is confusing and should be clarified in the benchmark descriptions to avoid misinterpretation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)