TaCo:  A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data
Authors: Zhengxue Cheng
Yan Zhao
Keyu Wang
Hengdi ZHANG
Li Song
Venue: ICLR 2026 Poster (2026)
Keywords: Tactile Dataset
Lossless Compression
Lossy Compression
Heterogeneous Tactile Data
Primary area: datasets and benchmarks
TL;DR: This paper presents the first comprehensive benchmark for Tactile data Codecs  across five representative tactile datasets.

Abstract: Tactile sensing is crucial for embodied intelligence, providing fine-grained perception and control in complex environments. However, efficient tactile data compression, which is essential for real-time robotic applications under strict bandwidth constraints, remains underexplored. The inherent heterogeneity and spatiotemporal complexity of tactile data further complicate this challenge. To bridge this gap, we introduce TaCo, the first comprehensive benchmark for Tactile data Codecs. TaCo evaluates 30 compression methods, including off-the-shelf compression algorithms and neural codecs, across five diverse datasets from various sensor types. We systematically assess both lossless and lossy compression schemes on four key tasks: lossless storage, human visualization, material and object classification, and dexterous robotic grasping. Notably, we pioneer the development of data-driven codecs explicitly trained on tactile data, TaCo-LL (lossless) and TaCo-L (lossy). Results have validated the superior performance of our TaCo-LL and TaCo-L. This benchmark provides a foundational framework for understanding the critical trade-offs between compression efficiency and task performance, paving the way for future advances in tactile perception.

Published as a conference paper at ICLR 2026
TACO: A BENCHMARK FOR LOSSLESS AND LossY CODECS OF HETEROGENEOUS TACTILE DATA
Keyu Wang's 1 Shanghai Jiao Tong University. Shanghai, China 2 Paxini Tech., Shenzhen, China zxchengês jtu. jtu.uuu.ccu
Zhengxue Cheng' Yan Zhao
Hengdi Zhang?
Li Song'
Necessity a Tactile Codec
Not Benchmarked!
Taxonomy of Tactile Codecs
TaCo:
Challenges Heterogeneity of Tactile Data
30 Codecs
OtyTa:
Transmission
Telroperction
5 Tactile Datasets 250K frames
Off-the-Shelf. Codecs (17)
Neural Codecs (13)....
High Redundancy
GZIP ZSTD WebP
DLPR
PaLLM
PNG
PLIP
LALIC TaCo-LL
BZIP2
types of Tasks
JPEG-XL (UA
HaL-cc
(Alumue)
TaCo-L
JPEG2000
ELIC DCVC-FM DeVe-DC
VTM-Iatro VTH-SCC
VVnnC
DualComp-1 DCVC-RT
Tectile-related Various Tasks for for Human, Machine and Robotics
Lossless Compression Storage
Lossy Compression - Human Vision / Classification Gresping
Teuch curgle
Demorosed Data: 10alipe
[~20X Lassless Compression)
(co.tbpp / /-4d8))
(o.bbpp/ / -95% aoc)
(o.osbpp/ / -60% SR)
The motivation of our TaCo benchmark, established through an extensive evaluation on tactile codees across multiple dimensions. First, assess 30 off off-the-shelf and neural codecs 5 heterogeneous tactile datasets with more than 250K frames. Second, we introduce purely-trained TaCo-LL TaCo-L codecs to field lossless and lossy tactile data compression. Finally, we evaluate the coding performance on 4 distinct task types designed to serve for human, machine, and rohotics.
Figure I:
explore the data-driven approaches
ABSTRACT
Tactile sensing is crucial for embodied intelligence, providing fine-grained per- ception and control complex environments. However, efficient tactile data compression, which is essential for real-time robotic applications under strict bandwidth constraints, remains underexplored. The inherent heterogeneity and spatiotemporal complexity of tactile data further complicate this challenge. To bridge this gap, we introduce TaCo, the first comprehensive benchmark for Tactile data Codecs. TaCo evaluates 30 compression methods, including off-the-shelf compression algorithms and neural codecs, across five diverse datasets from var- ious sensor types. We systematically assess both lossless and lossy compression schemes on four key tasks: lossless storage, human visualization, material and object classification, and dexterous robotic grasping. Notably, we pioneer the development of data-driven codecs explicitly trained on tactile data, TaCo-LL (lossless) and TaCo-L (lossy). Results have validated the superior performance of our TaCo-LL and TaCo-L. This benchmark provides foundational framework for understanding the critical trade-offs between compression efficiency and task performance, paving the way for future advances in tactile perception.
"Corresponding Author

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Published as a conference paper at ICLR 2026
INTRODUCTION
The acquisition and interpretation of tactile data are paramount for advancing embodied Al and achieving sophisticated human-machine interaction, as they provide the rich, physical context neces- sary for dexterous manipulation and awareness of the environment. However, the high-dimensional, spatio-temporally dense nature of tactile sensing results in rapidly growing data volumes, posing a significant bottleneck for real-time applications. Consequently, efficient tactile data compression critical for real-time haptic feedback dexterous hands, remote teleoperation, and large-scale storage of physical interactions for robotic model training.
While the imperative for efficient tactile data compression is well-established, current approaches remain diverse and fragmented. A corpus of existing research has explored this challenge through classical signal processing techniques (like dimensionality reduction and wavelet transforms), lever- aging transforms and codecs designed for speech image data. In recent years, data-driven methods have gradually gained popularity for their ability to learn optimal compact representations. Specif- ically, neural networks can learn compact latent representations in driven manner, enabling efficient lossless or lossy compression (Liu et al. 2023b Mentzer et al. 2019). Compared to tradi- tional codecs, neural compression offers greater flexibility and adaptability, particularly in scenarios involving complex or irregular signal structures (Ma et al.] 2019). These methods have been suc- cessfully applied in domains such as video and image compression (Zhao al., 2025| Feng al.) 2025a), but they are still unexplored for tactile data. Another difficulty in generating a data-driven codee for tactile datasets is the heterogeneity, arising from different sensing principles: visuo-tactile sensors such as Gelsight (Yuan et al.) 2017a) and DIGIT (Lambeta et al.) (0220) capture surface trans- formation, while other force-based sensors (Paxini 2025) measure force data. Therefore, the estab- lishment of comprehensive and open benchmark, comprising representative datasets, standardized evaluation metrics, and baseline models, is not merely beneficial but a necessary prerequisite for catalyzing advancements in this critical domain and enabling research.
As illustrated in Fig. [ we construct comprehensive benchmark to evaluate the compression per- formance of various codecs on heterogeneous tactile datasets. First, we collect five diverse tactile datasets, and 30 representative codecs. They include off-the-shelf codecs designed for text, image and video, aiming to remove the 1D and 2D, inter-frame and -rtra- redundancies. We also incorporate neural codecs pretrained on other modalities to assess their cross-domain generaliza- tion on tactile data. Second, we propose two data-driven codecs, i.e. TaCo-LI and TaCo-I,, which are trained from tactile datasets to learn intrinsic data patterns and exploit the redundancies in het- erogeneous tactile data. Third, we evaluate tactile compression performance using four types of tasks: lossless compression, lossy compression for human perception, semantic classification, and robot grasping. Experimental results validate the superior performance of our proposed data-driven models, TaCo-LL and TaCo-L, and we hope our benchmark will inspire further research in this field.
In summary, our main contributions are as follows.
We propose TaCo, the first comprehensive benehmark for tactile data codecs. It comprises five publicly tactile datasets, 30 codecs, and four tactile-related tasks: lossless compression, lossy compression for human visualization, tactile classification, and robotic grasping.
We introduce TaCo-LL and TaCo-L, the first purely data-driven tactile codecs, trained end-to-end on heterogencous tactile datascts to learn the intrinsic data distributions.
Extensive experimental results demonstrate that our proposed TaCo-LL and TaCo-I models surpass existing methods across all four tasks, establishing foundation for future research in the field of tactile data compression.
RELATED WORK
Tactile datasets play a key role in advancing robotic perception and manipula- tion, supporting tasks like grasping, object recognition, and material classification. Several recent datasets focus only on tactile signals (Liu & Ward-Cherrier, 2024| Zhao et al. 2024 Suresh et al,
Tactile Datasets.
2023 Yuan et al. 2018 Higuera et al. 2024 Schneider et al. 2025). TIP Bench (Liu & Ward- Cherrier 2024) converts sensor outputs into heatmaps and evaluates spatial acuity, stability, and generalization. FoTa (Zhao et al. 2024) merges multiple open datasets into unified collection of
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over three million samples. (CB-Slide (Suresh et al. ing interactions between a DIGIT tactile sensor and YCB objects. TacBench (Higuera et al. 2024) comprises 180,000+ unlabeled tactile images from surface-sliding sliding interactions, facilitating large- scale self-supervised learning. Tactile MNIST (Schneider et al. 2025) provides both simulated and real GelSight interactions for MNIST digits, including 13,580 3D-printable meshes and 153,600 lactile recordings, ActiveCloth (Yuan et al.) 2018)) comprises 6,616 robotic squeeze trials on 153 garments, recording synchronized GelSight tactile videos and Kinect depth with 11 attributes.
2023) records both simulated and real slid-
Beyond pure tactile sensing, some other recent works Yang el al. 2022 Feng et al. 2025b Liu
ct al., Cheng et al. 2025a Yu et al. 2024, Suresh al. 2024 Kerr et al. 2022; Yuan et al. 20. 7bj
Li et al. 2019 Gao et al.
et al. (025b) incorporate multi modal signals, combining
tactile with vision, language, or audio to support cross-modal learning. For instance, Touch and Go 2022 is the first large-scale tactile dataset collected in outdoor environments, captur- ing human interactions with natural objects via synchronized tactile and video data. VTnixManip (Liu et al.) provides 565,000 frames of video-tactile data from human multi-finger manipulations across 10 tasks and 182 objects, filling a gap in dexterous interaction datasets. Touch100K (Cheng jet al. 2025a) compiles and cleans TAG and VisGel data into 100,147 high-quality triplets, offer- ing the first large-scale alignment across tactile, visual, and linguistic modalities. TacQuad (Feng eet al., 2025b) integrates four visual-tactile sensors, recording aligned tactile signals, RGB frames, and GPT-generated textual descriptions for multimodal reasoning. ObjectForlder (Gao et al.) 2021) provides 100 neural object representations that encode 3D shape, appearance, sound, and tactile properties, supporting on-demand multimodal data generation for unified perception and control.
Yann et al.)
Tactile Compression. While tactile sensing continues to advance rapidly in resolution, sampling rate, and coverage, compression research for tactile data remains under-explored. Existing work has proposed some sparse or task-specific compression approaches (Hollis et al. 2016 Bartolozzi
let al. 2017 Hollis et al. 2018]] Shao et al.) 2020 Hassen et al.) 2020; Seeling et al. |02 Liu et al.,
2023a Slepyan et al. 2024 Li et al. 2025 Lu el al. (025). For instance, Shaao et al. (2020) exploits
the propagation of mechanical waves during dynamic contact to enable compact tactile encoding.
(Hassen et al. 2020) proposes perceptual vibrotactile codec that combines sparse linear prediction
with an acceleration sensitivity function. (Seeling et al. (022]) achieves real-time tactile compression by combining bit-level truncation with delta-coding driven by just-noticeable- differrenee thresholds.
Others like (Liu et al. 2023a) and (Slepyan et al. 2024) investigate dimensionality reduction via stacked auto-encoders or wavelet sparsification. However, these methods typically foeus simple signal sparsity or quantization strategies, often lack rigorous compression metrics and are tailored to relatively narrow scenarios or limited generalizability. In fact, many common tactile signals can be naturally transformed into image-like formats, enabling the use of standard image or general- purpose compressors. This direction is appealing not only because these compressors are well- established and widely available, but also because they offer tunable configurations to trade off compression ratio and distortion, making them adaptable to diverse robotic applications. Yet, this perspective remains largely under-explored in the tactile domain. To fill this gap, this paper presents a comprehensive benchmark for tactile compression methods, aiming to provide practical guidance and spark future research into efficient tactile data compression.
TACTILE DATASETS AND COMPRESSION METHODS
3.1
TACTILE DATASETS
We benchmark tactile compression across five representative datasets: Touch and Go (Yang et al. ObjectFolder 1.0 (Gao et al. 202T). SSVTP (Kerr et al.] 2022). YCB-Slide (Suresh et al., 2023 and ObjTac (Cheng et al., 2025b). These datasets span range of sensor types (vision- based and force-based), resolutions (from 120 x 160 to 640 x 480), and data scales, detailed in Table. [ Depending on the sensor type, tactile data exhibit strong structural heterogeneity, along with complex spatiotemporal correlations and redundancy, as illustrated in Fig.
2022
Specifically, the GelSight-based datasets (Touch and Go and ObjectFolder) and DIGIT- datasets (SSVTP and YCB-Slide are collected using vision-based tactile sensors that operate by illuminating a deformable elastomer surface with micro-LED arrays and capturing its surface de- formation through internal camera. This process converts tactile interactions into sequences of
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Table 1: Introduction of the utilized tactile datasets.
Dataset
#Objects #Frames
Resolution
Sensor
Touch and Go Yangg et al.
2022
3971
13.9K
640 480 x 30Hz GelSight Yuan al.
2017a
ObjectFolder 1.0 (Goo et al.
2021
100
100K
120 160 x 30Hz GelSight
Yuan et al.
2017a
SSVTP Kerr 2022
10
4.5K
240 x 320 x 30Hz DIGIT Lambeta et al.
2020
YCB-Slide Suresh al.
2023)
10
4.5K
240 x 320 x 30Hl DIGIT
Lambeta et al
2020
ObjTac CCheng ct al.
2025h
56
135K
5. x 12 x 200Hz
Force Sensor (Paxini
2025
RGB images videos, enabling direct applications of image or video compression techniques, In contrast, the ObjTac dataset collected using force-based tactile sensors. The sensor comprises N 60 contact points across the contact surface, each measuring a 3D force vector. These mea- surements form a temporally structured sequence of force data. To enable efficient compression, we map each 3D force vector RGB pixel and temporally stack the force readings across time duration T, generating images resolution x 60.
3.2 TACTILE COMPRESSION METHODS
We establish a benchmark for two categories of tactile codecs: 1) off-the-sheif codecs based on conventional signal processing, originally designed for general-purpose or visual data, and 2) neural codecs that leverage neural networks to learn data patterns end-to-end. As tactile signals are natively or transformable into image or video formats (see Section 3.1p. our evaluation of neural codees includes both pretrained image codecs and, to our knowledge, the first data-driven codecs explicitly trained on tactile datasets.
3.2.1 OFF-THE- SHELF COMPRESSION METHODS
Typically, off-the-shelf compression methods have been historically designed for text, image and video data. These classical techniques are fundamentally rooted in signal processing principles, aiming to eliminate statistical, spatial, or temporal redundancies present in 1D or 2D data.
Peneerl-PPurose Compression Methods. We evaluate three general-purpose lossless compres- gzip (Pasco. 1996). zstd (Meta] 2015), and bzip2 (Seward 2000), which are designed to exploit ID symbol redundancy using techniques such as dictionary coding (e.g.. L.777 in gzip and zstd), block-sorting transforms (e.g., Muurows-Wheeler in bzip2), and entropy coding.
Image and Video Compression Methods. When treating tactile data images, evaluate six standard image lossless codecs: PNG (Boutell 1997). FLIF (Sneyers) 2015), WebP (Google 2010), JPEG-XL (Team] 2021}, JPEG2000 (ISO/IEC 2000), and BPG (Bellard (0144) (the intra-mode mode of HEVC/265 codec). These image-specific compressors remove 2D spatial redundancy in images via predictive coding, transform coding (e.g., DCT wavelets), and context-based entropy coding.
In addition, we also evaluate six lossy image codecs: JPEG-XL (Team] 2021). JPEG2000 (ISO/IEC) 2000). well as the intra-frame and screen content coding (SCC) modes of HM Sullivan et al. 2012 and VTM (Bross et al. 2021) (i.c., HM-Intra, HM-SCC, VTM-Intra, VTM-SCC).
To further address the inter-frame redundancy in tactile data, evaluate three off-the-shell video codecs, VVenC (Bross et al., (22T)) x265 (Sullivan et al.] 2012), and SVT-AV1 (Han et al.) (02T). Due to the huge amount of video data, lossless video compression is rarely used in practice, so we only discuss lossy video compression methods.
3.2.2 NEURAL COMPRESSION METHODS
Recently, neural codecs have surpassed conventional codecs on text, image and video, owing to powerful learning capabilities of neural networks to the latent data distribution. However, tactile data exhibit unique statistical patterns, and heterogeneous tactile datasets involve different distribu- tions, potentially making pre-trained neural encoders less applicable. Herein, we briefly introduce the diagram of learning-based lossy and lossless neural codecs, Fig.
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Training Set
LossLess Codec: ToCo-LL
Lossy Codec: TaCo-L
a))
AE
8a
Q
Rg
QJAE
Teach
Test Set
Xci
pLy
Teash
30% FalerrFllor
8
9
Fa
AD
9x
AD
hg
AD
SG4TP
Ex-ppsl- I- P(0)
1=
Ey-por99 log, pb))
Figure 2: Diagram of data-driven compression methods and our proposed TaCo-I. and TaCo-I.L.
In the of lossless compression, the tactile signal is sequentially fed into neural network fo to predict the distribution of next symbol, p(z](=<<)) then is followed by an arithmetic encoder (AE) to generate bitstream. The loss is the entropy, which is the minimal bound to encode
C El logg (p(,
At the decoder side, the symbols can be lossless autoregressively decoded through arithmetic decoder (AD) and same network.
For neural lossy codecs, the tactile signal x: is transformed through a transform function Ja into a latent presentation y. Afterwards, y is quantized through Q to get discrete values y. then it is followed by AE generate bitstream. At the decoder side, is decoded from bitstream using AD and then transformed back to reconstructed images & though an inverse transform function J.- ha and h2 denote analysis and synthesis transforms in the hyper autoencoder to generate side bits 2, as a prior to estimate density model ýj The loss is defined as a sate-distortion function:
L x D(x, 2) + E[- log, (Po)= (
where À is hyper-ypaamne peper-paraeeter to control the bitrate, and all the parameters are learnable.
The lossless neural-based methods include 5 image compression mod- els, DLPR (Bai et al. 2024). ) P2LLM (Chen et al.) 2024). and DualComp-I (Zhao et al.) 2025), as well LMIC (Deletang et 2024), a multi-modality compressor based on pretrained large language models (specifially, in this paper we use RWKV-7B (Peng et al., 2025) and Llama3-8B (Al@ Meta 2024] as LLMs for implementation).
Pretrained Neural Codecs
worth noting that these models are pretrained on natural language or image data, and evaluated on tactile data without any domain-specifer adaptations.
For the purpose lossy neural-based compression approaches, we evaluate total of compression models, consisting of three recent neural-based image codees (ELIC (He et al., 2022). TCM (Liu al.) (023bb) and LALIC (Feng et al., 2025a), and three recent neural-based video compressors (DCVC-DC (Li et al. 2023) DCVC-FM (Li et al. 2024). and DCVC-RT (Jia al. 2025)
To our knowledge, there have been yet no existing methods fully trained on tactile signals explore the upper bound of tactile compression performance. To further explore the performance potential of data-driven codec, we retrain two state-of-the-art com- pression models, DualComp-I and LALIC, using tactile datasets. Specifically, DualComp-I operates lossless compression by tokenizing the input into discrete representations and applying an auto- regressive model to predict each token's 's distributions, enabling efficient entropy coding. LALIC implements lossy compression and follows variational auto-encoder (VAE) (Doersch) (0166) ar- chitecture. The two models are chosen for their competitive performance and efficiency in their respective domains. By retraining them on tactile data, we aim to assess the benefits of data-driven tactile compression. The retrained models are referred to as TaCo-LL (lossless) and TaCo-L (lossy), respectively, distinguish them from their original pretrained versions.
Tactile Data-Driven Neural Codecs
Specifically, for TaCo-LL, the tokenization conducted as shown in Fig. We divide the input into 16 x 16 x 3 patches preserve local spatial correlations. We then flatten the data in raster-scan order. For visuo-tactile data, including Touch and Go. YCB-slide, ObjectFolder, SSVTP, the RGB values sequentially expanded sub-pixels (Ri, G1, B1, R2, G2, B2.... For three-axis force
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LossLess Codec: TaCo-LL
Lossy Codec: TaCo-L
Visuo-Tacille Rs. P1. R2..
Force-Tacille < Ka Pg...
"Training Patch Stzee [B, 16716 [s]
"Training Patch Size: 256,
w/o Tokenization:
Tokenization: [8, H*W33]
[8. H. W,
Figure 3: Detailed implementations of our proposed TaCo-L and TaCo-LL.
signals, i.e. ObjTac, are treated as three color channels and expanded as (21 y1, 21, 22, V2, 22,' For TaCo-L, follow the setup of LALIC and randomly crop zero-pad the input tactile image to 256 x 256 resolution. Since the input tensor has three channels for both visuo-tactile data and force-tactile data, no tokenization is needed, as shown in Fig. 3 The network architecture adopted from the LALIC model (Feng et al. 2025a) and the Ja and Is consist of four downsampling and upsampling operations, respectively.
To this end, we benchmark a total of 30 codecs to evaluate the compressibility of tactile data. Among them, 14 codees (9 off-the-shen codecs, 4 neural codecs and one proposed TaCo-LL) support lossless compression, aiming to preserve exact signal fidelity. The remaining codecs (9 off-the-shell, 6 neural codecs and one proposed TaCo-L.) are lossy, targeting higher compression ratios the cost of some reconstruction distortion. These methods can also be categorized by their training data domain: 28 codecs are existing methods originally developed for without any tactile-specific adaptation, while the remaining two (TaCo-LL, TaCo-L) are data-driven models explicitly trained on tactile datasets. Evaluating the pretrained models allows us to assess how well existing compression techniques generalize to tactile data, whereas the tactile data-driven methods help explore the potential of tactile-aware compression strategies.
or visual data and applied
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We benchmark the performance of tactile compression five representative tactile datasets: Touch and Go, ObjectFolder 1.0, SSVTP, YCB-Slide, and ObjTac, as shown Section 3.T] Specifically, randomly select 70% of the data from the Touch and Go and ObjectFolder datasets train the TaCo-LL and TaCo-L models. The remaining 30% of the two datasets, together with the entire SSVTP, YCB-Slide and ObjTac datasets, are used for all methods' compression evaluation. Fol- lowing (Zhao et al., 2025), for TaCo-LL we use the FusedAdam optimizer (NVIDIA) 2018) with cosine annealing learning rate schedule Loshchilov & Hutter Hutter (011),, starting at 1 10 and decay- ing to 2 10-5 over 20 epochs. Following (Feng et al. 2025a). we train TaCo-L using the Adam optimizer (Kingma & Ba 2014). The learning rate is set to x 10-1 for 40 epochs, then decayed to 1 x 10 for another 4 epochs. The training performed on two NVIDIA A100 GPUs.
4.2 LOSSLESS COMPRESSION
Evaluation Metrics. We evaluate lossless compression efficiency using bits per Byte, which quan- tifies the number of bits required to encode one byte of the original tactile data. Lower bits/Byte values indicate more effective compression, with uncompressed data corresponding to 8 bits/Byte. We also compare the complexity of different algorithms using four metrics, i.c. model parameters, MACs, inference speed (KB/s) on multiple devices (including NVIDIA A100 GPU, MacBook Pro), and the frame per second (FPS) ranging with different spatial resolutions.
Results. Table. benchmarks the lossless compression performance across five tactile datasets. As expected, all methods obviously reduce the data's 's storage cost, but the degree of compression varies across the compressors and datasets. Among off-the-shelf baselines, pureras- compressors such as gzip and zstd achieve moderate compression ratios. Image-specific codecs like FLIF and JPEG-XL provide notably better results especially on vision-like tactile data, due to their ability to
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Table 2: Comparison of lossless compression performance (bits/Byte) five tactile datasets. The best results are highlighted in bold blue, second-best bold, and third fifth underline. denotes the model parameter. To show the compression performance more clearly, we also list the compression ratios relative to the uncompressed data (8 bits/Byte) in parentheses only for the hest and sccond best results.
TaCo, 12M/48M/96M
bits/Bytel
Compressor
TouchandGo
ObjectFolder
SSVTP
ObjTac
YCB YCB-Slide
uncompressed
8 (1x)
8 (1x)
(1x)
8 (1x)
8(1x)
gzip
Pasco
1996
2.298
3.969
2.234
0.571
2,185
zstd
Meta
2015
2.263
3.966
2.233
0.568
2,184
bzip2
Seward
2000
2.288
4.031
2.255
0.594
2.205
Off-the- Shelf
FLJF
Sneyers
2015
0.808 (10x)
3.765
1,567
0.363 (22x)
1,489 (5x)
Bellard
2014
3.726
0.513
WebP
Google
2010
0,936
3.612
1,820
0.424 (19x)
1,767
JPEG-XL Team
2021
0.739 (11x)
3.657
1478 (6x)
0.382 (21 x)
1.431 (6x)
JPEG2000
1SO/1BC
2000
1,552
3,989
1,997
1,399
1,916
PNG Boutelll 1997]
2,500
3.964
2,233
0.579
2,183
DLPR
al 20/24)
1,082
3.774
1,539
0.522
1.503
P2LLM
Chen et al
2024
1.212
3.400
1.804
0.546
1.512
Llama3*
Deletang al.
2024
2.055
3.465
1.975
0.834
1,905
Neural
RWKV
Deletang et al
2024
2,223
3.718
2.010
0.540
1.880
DualComp-I
Zhao
2025
(3x)
(6xx
0.540
1,388 (6x)
TaCo- 12M (ours)
0.622 (13x)
3.098 (3x)
1.457 (6x)
0.569
1,520
ToCo-LL-48M (ours)
0.504 (16x)
(3x)
(6x)
0.411 (20x)
1.321 (6x)
TaCo-LL-96M (ouTs)
0.447 (18x)
2,709 (3x)
1.066 (8x)
0,360 (22x)
1,073 (8x)
Table 3: The complexity of lossless compression algorithms five tactile datasets. f and È indicates speeds measured MacBook Pro CPU and NVIDIA A100 GPU, respectively.
Speed (KB/s)
Speed (FPS)
Compressor
#Params]
MACs)
TouchandGo ObjectFolder
SSVTP
ObjTac
YCB-Slide
g2ip
Pasco.
1996
14500
15.7!
252
62.91
1901
63,91
2std
Meta.
2015
11000
11.9t
1911
47.71
47.41
bzip2
Seward
2000
3300
3.58
57,31
14.3
43.31
14.3t
Off-the-She
FLIF
Sneyers
2015
6521
0.71
11.31
2.84
8.561
2,84
BPG
Bellard
2014
180
0.20
3.13*
0.78
2.36"
0.781
WebP
(Google
2010
330
0.36
5.73
1.43
4.33
1.43
JPEG-XL
Team
202
970
1.05*
16.8
4,21
12.71
4.21
JPEG2000
ISO/1BC
2000
5000
5.431
86,81
21.71
65.71
21.71
PNG (Boutell
2001
0.221
3.471
0,871
2.631
0.871
DLPR Bai al.
20244
22.3M
640
0.691
11.11
2.78
8.41
2.781
P2LLM
Chen et al.
2024
88
20
0.02
0.351
0.09
0.261
0.091
Ilama3*
Deletang
2024
8B
7.80
20F
0.023
0.35t
0.09F
0.26*
0.09*
Neural
RWKV Deletang et al
2024
7B
7.2G
86*
0.09*
1.49*
0
L.13
0,37
DualComp-I Zhao
2025
96M
59.9M
3174
0.34
5.50
1.38
4.16
1,38
T&Co-LL-I2M (ours)
12M
11.6M
614
0.671
10.74
2.66
8.06*
2.66
TaCo-LLL.48M (ours)
33.3M
360
0.39
6.25t
1.56
4.73
1.56
TBCo-LL-99MM (ours)
96M
59.9M
317*
0.34
5.50
1.38
4.161
1,381
exploit spatial correlations. Learning-basee methods pretrained natural images, such as DLPR, P2ILMM, and DualComp-I, effectively capture intra-frame correlations in tactile signals and gener- ally provide pleasing results. However, their performance remains limited by domain mismatch, especially on non-visual or structurally different tactile datasets.
To further explore the potential of data-driven compression, we retrain state-of-the-as lossless image compressor, DualComp-I, on tactile datasets and obtain our TaCo-LL model. The largest variant, TaCo-LL-96M, achieves the best performance across all five datasets, reaching 0.447 bits/Byte TouchandGo. 2.709 bits/Byte on ObjectFolder, 1.066 on SSVTP, 0.360 bits/Byte on ObjTac, and 1.073 on TCB-Slide, corresponding 18x, 3x, 8x, 22x, and 8x compression ratios, respectively.

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Published as a conference paper at ICLR 2026
Table 4:
Evaluation of lossy compression performance five tactile datasets leveraging intra-frame compres- sors. best results are shown in blue bold, second-best in bold, and the third-best in underline. the reference, the bandwidth consumption of the anchor HEVC-intra is approximately 2Mbps at the quality of
40dB, which is calculated by 0.22 bit per pixel x640 x 480 30fps 10 for Touch and Go datasct, as Fig.
BD-Rate (%)
Compressor
TouchandGo
ObjectFolder
SSVTP
YCB-Slide Slide
YCB
ObjTac
HM-Intra
Sullivan et al.
2012
0%
0%
0%
Off-the-Seee Shetf
HM-SCC Sullivan al
2012
-10.4%
2.0%
6.9%
7.2%
-4.55
VTM-Intra
Bross ct al
202
-21 2.1.7%
-19.7%
-16.0%
-24.4%
-22.0%
VTM-SCC
Bross
212
-23.7%
-18.0%
-13.7%
-1911%
44.3%
JPEG-XL
Team 2021
66.7%
60.6%
77.5%
96.9%
99.4%
JPEG2000 [ISO/IEC
2000
59.7%
69.9%
107.9%
89.1%
103.8%
ELIC He 2022
-40.2%
0.6%
-5.8%
-9,2%
44.5%
Ncural
LALIC [Feng al.
2025a
-51.6%
-4.6%
32.8%
TCM (Liu et 2023b
-39.9%
23.7%
42.9%
30.5%
97.2%
TaCo-L (Ours)
-61.8%
-24.3%
-19.2%
-27.4%
-27.0%
Table 5: The complexity lossy compression algorithms on five tactile datasets leveraging intra-frame com- pressors. ft and & indicates speeds measured on MacBook Pro CPU and NVIDIA A GPU, respectively.
Speed (KB/s)
Speed (FPS)
Compressor
#Params MACs}
TouchandCio
ObjectFolder
YCB-Slide
ObfTac
HM-Intru
Sullivan al.
2012
11.1
0.12
1,971
1,491
0.50*
OfT-the-Son Sehe--Shiif
HM-SCC
Sullivan al.
2012
31.31
0.341
0.561
0,411
0.421
0,41
VTM-Intra
9.221
0.101
1.631
0.411
1.231
0.41
VTM-SCC
2021
4.61 1
0.051
0.72
0.18
0.55
0.18
JPEG-XL Team 2021
2305
2.501
40.0
10.01
30.21
10.0
JPEC2200 ISO/TEC
2000)
13200
14.31
2281
57.11
1721
57.11
ELIC He
2022
33.3M
0.9M
4075*
4.42
70.73
17.7
53.5
17.7
Neural
LALIC
2025a
63.2M
0.7M
3700
4.01
64.2
16.1
48.6
16.1
TCM
Liu al.
2023b
75.9M
1.8M
5680
6.16
98.61
24.71
74.6*
24.71
TaCo-L (Ours)
63.2M
0.73M
3700
4.01
64.2
16.1
48.6*
16.1
Table. benchmarks the complexity of different compression algorithms. Off-the-shelf codecs can achieve relatively fast speed. Among neural codecs, TaCo-LL models achieve competitive compres- sion performance with fewer parameters compared to P2LLM, Llama3-88 8BB and RWKV-7B, and the encoding/decoddng speed ranges from 317KB/s 614KB/s.
4.3 LOSSY COMPRESSION FOR HUMAN VISION
Evaluation Metrics. We evaluate lossy compression performance using the Bjøntegaard Delta Rate (BD-Rate) Bjontegaard 2001) metric, which quantifies the average bitrate savings at given level of distortion. A lower BD-Rate indicates better compression efficiency. We measure the recon- struction distortion using Peak Signal-to-Noise Ratio (PSNR) (Rosenfeld & Kak 1982). The bitrate is assessed in bits per pixel (BPP), where uncompressed data corresponds to 24 BPP.
Results. Table. benchmarks the lossy compression performance when using intra-frame com- pressors. Off-the-shelf intra-frame codecs like HM-Intra and VTM-Intra provide strong base- lines, consistently delivering competitive performance. seneral-purpose codees like JPEG2000 and JPEG-XL are included as standard baselines, but their performance is relatively poor. Neural com- pression methods pretrained on natural images, such as ELIC, LALIC, and TCM, show promising results some datasets, but they fail to generalize to more structurally distinct data like ObjTac. In contrast, our TaCo-L model, trained on tactile datasets, achieves the best performance across all five datasets. It outperforms all baselines with BD-Rate reductions of -61.8% (TouchandGo), 24.3% (ObjectFolder), -27.4% YCBdel and -2.0% (ObjTac). Further, the force-based ObjTac dataset, which is derived from 3D force signals and mapped into RGB images, exhibits character-
8

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Published as a conference paper at ICLR 2026
--- TaCo-L -+-LALIC -B-BLIC -a- TCM VIM-hum - VTM-SCC - H--H---atra --- HM-SCC - JPE-XL
PPC22000
Touch and Go
Touch and Go
42
USS-SIMM (dB
19.5
PSNR (dB)
40
18.0
38
16.5
36
32
Bitrate (bpp)
13.5
Bitrate (bpp)
0.05
0.10
0.15
0.20
0.25
0.05
0.30
0.15
0.20
Figure 4: Rate-distortion curves on TouchandGo dataset, when applying intra-frame compression methods.
Table 6: Material classification results on TouchandGo, ObjectFolder- 1.0 and object classification results on
YCB-Slide, Best results are blue bold, the second-best results are bold, and the third-bess in underline.
Compressor
HPP
SVM
Random Forest
K-NN
Linear Regression
Touch and Go
Uncompressed
24 x1)
74.88%
68.24%
73.51
VTM-Intra
0.213
74.08%
72.53%
65.06%
70.87%
JPEG-XL
0.218
70.67%
69.43%
61.87%
67.75%
LALIC
0,196
74.70%
73.07%
65.43%
71.24%
TaCo-L (ours)
0.193 (124x)
75.12%
73.55%
66.03%
71.89 %
Uncompressed
(1x)
44.11%
40.68%
37.14%
42.92%
ObjectFalder
VTM-Intra
0.384
42.23%
39.48%
36.00%
40.71%
JPEG-XI
40.27%
37.63%
38.74%
LALIC
0.477
41.00%
38.28%
35.44%
39.91% 166
TaCo-L (nurs)
(53x)
43.08%
39.85%
36.27%
41.02%
Uncompressed
24
98.75%
98.72% %
98.58%
99.18%
YCB-Slide
VTM-Intra
0.118
97.36%
96.41 %
97.08%
97.24%
JPEG-XL
0.121
94.08%
93.11%
93.67%
93.97%
LALIC
0.130
95.67%
95.22%
95.76%
96.23%
TaCo-L (ours)
0.126 (190x)
98.01
97.35%
97.88%
98.20%
istics similar to screen content (large uniform regions and repetitive patterns). This makes screen- content-optimized codecs, VTM-SCC and HM-SCC, particularly effective on this dataset, achiev- ing BD-Rates of -44.3% and -44.5%, respectively. when taking HM-Intra as the anchor. Table, [ benchmarks the complexity of different lossy compression algorithms. For off thethe codecs, the complexity increases along with the development of newer generations. For neural codecs, TaCo-L, adapted from the latest LALIC, achieves the best compression performance the cost of incremen- tal complexity, and the encoding/decoding FPS ranges from 4 FPS to 48 FPS at different resolutions. Fig. 4 presents representative rate-distortion (RD) curve comparison the TouchandGo dataset when using image compressors. TaCo-L achieves the SoTA performance across various bitrates.
4.4 LOSSY COMPRESSION FOR CLASSIFICATION
Evaluation Metrics. We evaluate the semantic fidelity of lossy compression using two tactile understanding tasks: material classification (on TouchandGio and ObjectFolder- and object clas- sification (on VCB-Slide). For each dataset, Random Forest Rigatti (017).. K-NN (Peterson 2009), and Linear Regression (Seber & Lee 2012 with a fixed 60%/40% train-test data split. The top-1 accuracy is used as evaluation metric. Four representative lossy codecs, VTM-Intra, JPEG-XL, LALIC, and TaCo-L, are used for comparison.
four standard classifiers, SVM (Burges) 1998).
Results. Table. 6 shown in Table. 6 all methods achieve classification performance close to the uncompressed data, despite substantial bitrate savings (e.g., from 24 bpp to as low as 0.118 bpp). On TouchandGo, TaCo-L achieves 75.12%% (SVM) and 71.89% (Linear Regression), similar to 76.63% and 73.51% when using uncompressed data. On ObjectFolder, where the task is more challenging, the top-1 accuracy under SVM drops slightly from 44.1 % to 43.08% after compression with TaCo- L. On YCB-Slide, TaCo-L also preserves superior classification accuracy (98.01 and 98.20% when using SVM and Linear Regression, respectively), while reducing the bitrate by 190x.

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Published as a conference paper at ICLR 2026
Table 7: Evaluation results the dexterous grasping. Best results shown in blue bold, the second -est results are denoted in bold, and the third-best underline. We also list accuracy loss relative the uncom- pressed data (8 bits/Byte) in parentheses.
Compressor
BPP
Small Obj.
Medium Obj.
Large Obj.
Deform. Obj.
Avg
Uncompressed
54.7%
63.9%
63.8% (-0.0%
JPEG-XL
0.0505
47.2%
58.0%
59.7%
55.1%
55.0% (-8.8%)
VTM-Intru
0.0498
54.1
66.6%
68.4%
63.1
63.1% (-0.7%)
LALIC
0.0397
51.5%
63.4%
65 65.0%
60.1%
60.0% (-3.8%)
TaCo-L (ours)
0.0251
53.1%
65.3%
68.4% %
61.9%
62.2% (-1.6%)
Uncompressed
24
51.8%
65.8%
67.3%
61.8%
61.7% (-0.0%)
JPEG-XL
0.0505
46.4%
56.1%
$7.4%
52.8%
53.2% (-8,5%)
Shaarh
VTM-Intra
52.5%
65.0%
66.7%
61.1%
61.3% (-0.4%)
LALIC
0.0397
49.9%
61.8%
63.1%
58.0%%
58.2% (-3.5%)
TaCo-L (ours)
0.0251
51.3%
63.6%
65.0%
59.7%
59.9% (-188%
4.5
LOSSY COMPRESSION FOR DEXTEROUS GRASPING
Task Definition. Many contact-rich manipulation algorithms for dexterous hands rely heavily on high-fidelity tactile signals, which motivates us to conduct dexterous grasping experiment. In real- world deployment scenarios, however, tactile data compression may affect the downstream perfor- mance of such algorithms. Therefore, we introduce this experiment to evaluate the impact of tactile compression quality realistic, task-driven benchmark. The goal of this task is to reach for an ob- ject, grasp and lift it. We build the simulation using Nvidia IssacSim Makoviychuk et al. (202I), and use simple DexHand13 module Paxini. (2024) equipped with eleven tactile sensors. We modify the input tactile signals by compressing them first and then feed into a tactile-aware reinforcement learning algorithm. In total
100 objects evaluate the grasping performance, consisting of 29 small objects, 41 medium objects and 22 large objects, 8 deformable objects. For the following section, we list the performance for each category.
Evaluation Metrics. To ensure robust interference capabilities during grasping, we evaluate the performance using two evaluation metrics in the simulation. One is the success of lifting 8in. recorded when objects maintain stability lifted to the height of 0.1m. The other is the success rate with disturbance resistance Sdistub, measured by applying 2.5N external forces along six axes for s after lifting and the object moves below 0.02 m.
Results. Table. benchmarks the grasping performance across different objects. Since the tactile signal simulated by Isaac Sim is relatively sparse, the achieved compression ratio is higher (up to 1000x) than what typically attainable the physical world (i.e. physical data achieve at most 22x (ObjTac) compression ratio). All compression methods successfully reduced the raw 24 bpp tactile signal to substantially smaller sizes, ranging from 0.025 bpp to 0.5 bpp. with only moder- ate decrement in grasping success rate. Among them, TaCo-L outperforms JPEG-XL and LALIC, achieving a higher compression ratio while maintaining competitive lifting success rate of 62.2% compared to the baseline 63.8% and disturb-resistant grasping success rate of 59.9% compared to the baseline 61.7%. While our method TaCo-L. only approximately 1% of VTM's performance (62.2% vs 63.1% % and 59.9 9% 61.3%) in terms of task success rate, it achieves significantly higher compression efficiency by operating at nearly half the bitrate (0.0251bpp vs. 0.0498 bpp).
CONCLUSION
This paper introduced the TaCo benchmark, the first comprehensive framework for evaluating tactile data codecs. This is a suite of 30 codecs, 5 datasets and types of tasks to advance the research on tactile sensing and tactile data compression. Further, we presented TaCo-LL and TaCo-L, data- driven codecs that learn the latent distribution of tactile data end-to-end. Extensive experiments demonstrate that proposed models establish new state-of-the art result, outperforming existing methods across lossless/lossy compression, classification, and grasping tasks. Our work provides a critical foundation and baseline for future research in efficient tactile perception and transmission.
10

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENT
This work was partly supported by the NSFC (62431015, 62571317, 62501387), the Fundamental Research Funds for the Central Universities, Shanghai Key Laboratory of Digital Media Processing and Transmission under Grant 22D22229005, 111 project BP07 19010 and Okawa Research Grant.
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Published as a conference paper at ICLR 2026
Sudharshan Suresh, Haozhi Qi, Tingfan Wu, Taosha Fan, Luis Pineda, Mike Lambeta, Jitendra Malik, Mrinal Kalakrishnan, Roberto Calandra, Michael Kaess, ct. Neuralfoels with neural fields; Visuotactile perception for in-hand manipulation. Science Robotics, 9(9))cadd0628, 2024.
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Samson Kelvin Lin, Anxing Xiao, Jiafei Duan, Harold Soh. Octopi: Object property reasoning with large tactill-language models. arXiv preprint arXiv:2405.02940 2024.
Wenzhen Yuan, Siyuan Dong, Edward H Adelson. Gelsight: High-resolution robot tactile sensors estimating geometry and force. Sensors, 17(12):2762, 2017a.
Wenzhen Yuan, Shaoxiong Wang, Siyuan Dong, Edward Adelson. Connecting look and feel: Associating the visual and tactile properties of physical materials, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
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Wenzhen Yuan, Yuchen Mo, Shaoxiong Wang. and Edward H Adelson. Active clothing material perception using tactile sensing and deep learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4842-4849. IEEE, 2018.
Jialiang Zhao, Yuxiang Ma. Lirui Wang, and Edward H Adelson. Transferable tactile transformers for repre- sentation learning across diverse sensors and tasks. arXiv preprint arXiv:2406.13644 13640, 2024.
Yan Zhao, Zhengxue Cheng, Junxuan Zhang, Qunshan Gu, Qi Wang, and Li Song. Dualcomp: End-to-end learning of unified tual-modaliy lossless compressor. arXiv preprint arXiv:2505. 2025.
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Reviews:
[{"review_id": "JS01QX45nd", "reviewer": "Reviewer_bciL", "summary": "This paper proposed TaCo as the first comprehensive benchmark for evaluating tactile data codecs. \nTaCo evaluates existing classical and neural codecs on lossless storage, human visualization, material and object classification, and dexterous robotic grasping. \nThe authors also proposed TaCo-LL (lossless) and TaCo-L (lossy) models and achieved superior performance on the benchmark.", "questions": "Covered in \"Weaknesses\".", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The motivation is important for the community. Bandwidth requirement has been one of the main bottlenecks for vision-based tactile sensors nowadays, whose temporal resolution is usually limited despite the high spatial resolution. A benchmark for tactile codecs is necessary to push forward the research in this direction. The benchmark design is comprehensive and covers a wide range of tasks that are important for tactile data usage, making it a valuable resource for the community.", "weaknesses": "1. Still not enough discussion/experiment to support the motivation. To decrease the bandwidth requirement for tactile hands in reality, a discussion/metric on whether/how these methods are computationally feasible on the sensor side hardware is necessary. Without that, the motivation of this direction is vague.\n2. Lack details about the method itself. \"Symbol\" predicted by $f_a$ is not defined anywhere nor is its dimension or quantization level for AE clarified. Neural nets used in either setting not explained. Lack high level explanation in Sec 3.2.2.\n3. Writing issues, including but not limited to: Typos; Citation formats messed up; Missing full stops; Lack of explanation of abbreviations; Eq. 1 is incomplete; Table 2 lacks explanation on what does your -12M/48M/96M mean, and why some of the results has a ratio after it but some doesn't. How is Table 4 related to the topic of the paper is unclear.\n4. Lack qualitative results. As the only figure in the paper that shows qualitative results, Fig 6 is way too subtle that I cannot see the difference. Also I highly doubt the quality of tactile signals you generated within Isaac Sim. On the codec aspect, the Sim2Real gap for tactile simulation is not ignorable in this case. The lack of artifacts and much simpler optical setup could make signal behavior far from real world scenarios. On simulation itself, can you show qualitative examples to prove the results are reasonable, especially for those deformable-deformable contacts you claimed?\n5. For the quantitative results, especially the dexterous hand grasping experiment, what's the purpose of introducing $s_{disturb}$ experiment? Can you explicitly explain that in your experiment design section? I also don't think the result is \"comparable\" to VTM-Intra.", "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": "BbXmAkNUUI", "reviewer": "Reviewer_ieBG", "summary": "This paper introduces TaCo, a comprehensive benchmark for tactile data compression. TaCo evaluates 30 codecs (17 off-the-shelf, 13 neural) across five heterogeneous tactile datasets comprising over 250K samples.The benchmark measures both lossless and lossy compression, connecting performance to four downstream tasks: data storage, human visualization, material/object classification, and robotic grasping. Furthermore, the authors train two tactile-specific codecs, TaCo-LL (lossless) and TaCo-L (lossy), adapted from DualComp and LALIC, showing that modality-specific training can substantially improve compression efficiency and downstream task fidelity.", "questions": "1. What specific reasons motivated the choice of ObjectFolder (v1) instead of the newer ObjectFolder 2.0, which features higher scene diversity and richer material representations?\n Were there technical constraints (e.g., data volume, format consistency), or does ObjectFolder 2.0 differ in ways that make it unsuitable for this benchmark?\n2. What specific modifications were made to DualComp and LALIC architectures when adapting them to tactile data?\n Please clarify in detail whether the entropy modeling, tokenization, or context mechanisms differ from the originals.\n3. How were the grasping experiments conducted with respect to compression?\nSection 4.5 describes a grasping simulation built in NVIDIA Isaac Sim using DexHand-13 with 11 tactile sensors, where pre-compressed tactile signals are fed into a tactile-aware RL controller. Could you provide additional details such as the tactile sampling rate, control frequency, and how compression latency was incorporated into the control loop?\n Moreover, robotic grasping tasks are particularly sensitive to real-world tactile performance—success rate, contact stability, and control frequency often differ substantially between simulation and physical setups. It would therefore be valuable to discuss or empirically examine how the proposed codecs might behave on real robotic hardware, even at small scale, to assess practical applicability beyond simulation.\n4. Could you report the inference-time throughput (e.g., KB/s) and latency of TaCo-LL and TaCo-L compared with existing codecs?\n Since DualComp—one of the models adapted in this work—emphasizes runtime efficiency and reports detailed KB/s metrics, including similar results here would clarify whether the proposed tactile codecs preserve comparable efficiency. This metric is also particularly important for real-time robotic systems.\n5. Have you examined how sensitive TaCo-LL and TaCo-L are to the training data distribution (e.g., varying tactile sensor types or object categories)?\n This could clarify whether performance gains arise from data diversity or from architectural suitability.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Addresses a clear and underexplored bottleneck.\nTaCo fills a critical gap in the tactile perception community: the absence of a standardized, quantitative framework for evaluating tactile data compression.The scope—five tactile datasets, multiple codecs, and four downstream tasks—demonstrates strong engineering execution and community value.\n2. Task-grounded evaluation beyond compression metrics.\nThe inclusion of real downstream metrics (classification, grasping) moves the discussion beyond raw rate–distortion numbers and demonstrates practical significance for embodied and robotic systems.\n3. Evidence that tactile-specific retraining is beneficial.\nThe adapted models (TaCo-LL, TaCo-L) consistently outperform generic codecs across modalities, indicating that learning tactile-specific priors is nontrivial and valuable. This establishes a solid empirical foundation for future work on modality-aware compression.", "weaknesses": "1. Limited methodological originality.\n While the benchmark is extensive, the codec architectures themselves largely reuse existing designs (DualComp and LALIC) with dataset-specific retraining. The paper’s main novelty lies in benchmarking and empirical synthesis rather than proposing new modeling principles or theoretical insights into tactile compression.\n2. Insufficient analysis of tactile data characteristics.\n The paper shows that tactile-specific codecs outperform generic ones but does not investigate the underlying statistical or structural differences that drive this improvement. Analyses such as entropy distribution, temporal redundancy, or spatial frequency spectra could provide insight into why certain codecs perform better on specific tactile modalities.\n3. Lack of robustness and generalization analysis.\n The models are trained on a subset of datasets and evaluated on others, but there is no quantitative assessment of domain shift or variability (e.g., performance variance across seeds, datasets, or other sensor types such as vibrotactile, event-based, or thermal sensing are not included). Without such analysis, the extent to which the proposed codecs generalize beyond the training distribution remains unclear.\n4. Missing runtime and deployment evaluation.\n Although model sizes are reported, the inference time such as KB/s or other metrics related to time efficiency is not analyzed. These aspects are important for understanding whether the proposed codecs are suitable for real-time robotic applications.", "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": "DRmv14muuR", "reviewer": "Reviewer_zy4w", "summary": "This paper introduce TaCo, the first comprehensive benchmark for Tactile data Codecs.  It comprises five publicly tactile datasets, 30 codecs, and four tactile-related tasks. It  introduce two data-driven tactile codecs TaCo-LL and TaCo-L, and conducts experiment to validate their performance.", "questions": "1. In the appendix's motivation section, it is mentioned that one key challenge is that tactile data from high-resolution sensor arrays on robotic hands can consume a large portion of the available bandwidth. I would like to know how this problem can be addressed using tactile compression algorithms, especially for vision-based tactile sensors that are essentially cameras.\n2. There are already some LLMs capable of handling tactile inputs [1,2]. Why not consider using these methods instead of using LLaMA, which has never been exposed to tactile data?\n3. The experimental results on YCB-Slide are extremely high, with the uncompressed baseline reaching 99%. Is there an issue with the data split in the experiment?\n\n\n\n[1] Yang, Fengyu, et al. \"Binding touch to everything: Learning unified multimodal tactile representations.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024.\n\n[2] Yu, Samson, et al. \"Octopi: Object property reasoning with large tactile-language models.\" *arXiv preprint arXiv:2405.02794* (2024).", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. The proposed Toco benchmark is the first systematic benchmark specifically designed for tactile data compression.\n2. The paper addresses the problem of tactile data compression, which is indeed an important yet overlooked issue.\n3. The dataset encompasses a variety of tactile sensors.\n4. The work evaluates the impact of tactile data compression on several downstream tasks.", "weaknesses": "1. The practical contribution of this work is rather limited. The proposed TaCo benchmark is essentially a simple aggregation of several existing datasets, without introducing any new data or labels. The proposed TaCo-L and TaCo-LL are in fact direct applications of two existing methods, DualComp-I and LALIC, trained on tactile data. Essentially, this work only integrates existing datasets to form a new dataset specifically for the tactile data compression task.\n2. In both Figure 1 and the introduction, the paper highlights the significant intra-frame and inter-frame correlations in tactile videos, which should be a key distinction between tactile videos and natural RGB videos. However, disappointingly, the paper barely discusses this crucial aspect in its methodology, experiments, or metric design. Instead, it simply adopts existing approaches from natural image and video compression and applies them directly to the tactile domain.\n3. An important distinction between tactile data and natural image or video modalities is that tactile sensing mainly serves object interaction. Therefore, validating performance only on material classification tasks or simple grasping tasks in simulation environments, which are not closely related to real tactile interaction, is insufficient to demonstrate its effectiveness. More real-world object manipulation experiments should be included.\n4. A prominent issue in the tactile domain is the low diversity of available data and the significant heterogeneity among sensors. Does the proposed tactile data compression algorithm possess cross-sensor and unseen object generalization capabilities (the latter being particularly important)? Because in real-world applications, the objects that tactile sensors come into contact with can be highly diverse.\n5. Another key point is that tactile data cannot exist independently of objects. Therefore, it is important to compare and discuss methods such as ObjectFolder, which use implicit neural representations to model objects. Compared with these methods, do tactile compression algorithms also offer advantages in terms of quality and efficiency?\n6. Would tactile data compression negatively affect the spatially dependent information in tactile data? This aspect is crucial for tactile sensing, which is a highly fine-grained, sensitive, and localized modality during object manipulation.\n7. When applying tactile compression algorithms to compress tactile data for downstream tasks, how does this differ from and what advantages does it have over using pretrained tactile encoders (such as UniTouch [1], T3 [2], or AnyTouch [3]) for feature preprocessing (which also can be seen as compressing)? What kinds of problems specifically require tactile compression algorithms rather than these pretrained encoder-based methods? Possible experimental results should be provided to support the claims, along with thorough and detailed discussions.\n8. There are some confusing aspects in the experimental setup. For the Touch and Go dataset, since each recording segment contains many frames of the same object, the official split is based on collection trajectories. Why does this work not follow the official split and instead choose to perform a random split? In addition, the ObjectFolder dataset is a multimodal object dataset based on implicit neural representations, so the procedure for the material recognition task in this part needs to be explained in detail.\n\nOverall, I believe the contribution of this work is quite limited. It simply combined several existing tactile datasets and revalidated existing techniques and experiences from image and video compression in the tactile domain, while overlooking many unique characteristics of tactile sensing. Therefore, considering the numerous issues present in the paper and the high quality standards of ICLR, I am inclined to reject this paper unless the authors can fully address these problems within a short period of time, including adding additional experiments and extensive discussions.\n\n\n\n[1] Yang, Fengyu, et al. \"Binding touch to everything: Learning unified multimodal tactile representations.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024.\n\n[2] Zhao, Jialiang, et al. \"Transferable tactile transformers for representation learning across diverse sensors and tasks.\" *arXiv preprint arXiv:2406.13640* (2024).\n\n[3] Feng, Ruoxuan, et al. \"Anytouch: Learning unified static-dynamic representation across multiple visuo-tactile sensors.\" *arXiv preprint arXiv:2502.12191* (2025).", "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": "6hlLnguiFn", "reviewer": "Reviewer_RqzN", "summary": "This paper introduces TaCo, the first large-scale benchmark for tactile data compression, evaluating 30 codecs (off-the-shelf and neural) on five heterogeneous tactile datasets. It also proposes two data-driven codecs, TaCo-LL (lossless) and TaCo-L (lossy), trained end-to-end on tactile data. The benchmark spans four task types: lossless storage, human visualization, classification, and dexterous grasping. Extensive experiments show that the proposed codecs outperform existing methods across all tasks.", "questions": "Figure 6 shows “visual” quality at 0.06–0.09 bpp, but tactile images are not human-interpretable. Replace with a tactile-specific error map (e.g., normal-force RMSE) or skip visual inspection altogether.\nTable 3 uses BD-rate with HM-Intra as anchor. Please also quote absolute BPP at equivalent PSNR (e.g., 40 dB) so readers can judge real-world bandwidth (e.g., 0.04 bpp ≈ 1.2 Mbps at 30 fps).\nSection 4.5 employs Isaac Sim tactile signals that are artificially sparse (hence 1000× ratio). Clarify that physical GelSight data achieve at most 22× (ObjTac) to avoid misleading roboticists.\nTypos: “intra-frame compressors” header in Table 4 is misspelled “Jlrra-gt”; reference “Deletang et al. 2024” appears three times with inconsistent years.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Timeliness and Relevance \nTactile sensing is rapidly maturing, yet compression remains fragmented and under-studied. A unified benchmark is sorely needed; \nTaCo fills this gap convincingly.\nComprehensive Evaluation\nThe paper tests 30 codecs on >250 k frames from five datasets covering vision-based (GelSight, DIGIT) and force-based sensors, providing the broadest coverage to date.\nNovel Data-Driven Codecs\nTaCo-LL and TaCo-L are, to my knowledge, the first codecs trained exclusively on tactile data. They achieve new state-of-the-art bit-rates (e.g., 0.447 bits/Byte on Touch-and-Go, 18× compression) while preserving task accuracy.", "weaknesses": "Limited Physical Interpretation of Distortion\nPSNR and MS-SSIM are used for “human vision” evaluation, but tactile images are seldom viewed by humans; their perceptual space is task-dependent (e.g., shear vs. normal force). The paper does not validate that a 3 dB PSNR drop translates to an imperceptible change for either human fingers or downstream controllers. A small discrimination experiment (e.g., JND for contact edge localization) would strengthen the lossy compression claims.\n\nDataset Bias and Generalisation\nAll five datasets are collected on rigid, Lambertian objects. Soft or textured materials (foam, fabric, skin) exhibit elasto-plastic deformation and specularity that violate the stationary-signal assumption of both CNN and transformer codecs. The claim “superior on heterogeneous tactile data” is therefore premature. At minimum, the authors should report cross-dataset transfer (e.g., TaCo-L trained on Touch-and-Go, tested on ActiveCloth).\nBaseline Fairness for Neural Codecs\nPre-trained image codecs (ELIC, LALIC, TCM) are evaluated zero-shot, whereas TaCo-L is fine-tuned on the target domain. This gives an unfair advantage. A fair comparison would fine-tune all neural baselines for the same number of epochs or report the “pre-trained → tactile” transfer gap.\n\nAbsence of Latency/Complexity Metrics\nReal-time tactile feedback for tele-operation requires <1 ms end-to-end latency. The paper omits encode/decode runtime, GPU/CPU memory, and whether the arithmetic coder is single-pass. Without these numbers, a 190× compression ratio is meaningless for closed-loop control. Please add throughput (fps) vs. bitrate curves on an embedded GPU (Jetson Orin) or DSP.\nStatistical Significance in Grasping Experiments\nTable 6 reports success rates on 100 simulated objects. With only 8 deformable objects, the 95 % CI on the 62.2 % success rate is ≈ ±9 %, overlapping the baseline 63.8 %. The authors should use paired t-tests across seeds or bootstrap CIs to confirm that TaCo-L is not worse than uncompressed data.", "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)