# TaCo: A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data

arXiv: https://arxiv.org/abs/2602.09893 · HTML: https://mlsanity.com/paper/2602.09893/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 67 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 30 of 35)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [RWKV-7 "Goose" with Expressive Dynamic State Evolution](https://mlsanity.com/paper/2503.14456/) (2025)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [Practical Full Resolution Learned Lossless Image Compression](https://mlsanity.com/paper/1811.12817/) (2018)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [Image and Video Compression with Neural Networks: A Review](https://mlsanity.com/paper/1904.03567/) (2019)
- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer](https://mlsanity.com/paper/2204.02389/) (2022)
- [ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations](https://mlsanity.com/paper/2109.07991/) (2021)
- [Connecting Touch and Vision via Cross-Modal Prediction](https://mlsanity.com/paper/1906.06322/) (2019)
- [Sparsh: Self-supervised touch representations for vision-based tactile sensing](https://mlsanity.com/paper/2410.24090/) (2024)
- [Octopi: Object Property Reasoning with Large Tactile-Language Models](https://mlsanity.com/paper/2405.02794/) (2024)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features](https://mlsanity.com/paper/2209.13042/) (2022)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [MidasTouch: Monte-Carlo inference over distributions across sliding touch](https://mlsanity.com/paper/2210.14210/) (2022)
- [Active Clothing Material Perception using Tactile Sensing and Deep Learning](https://mlsanity.com/paper/1711.00574/) (2017)
- [Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation](https://mlsanity.com/paper/2406.03813/) (2024)
- [Linear Attention Modeling for Learned Image Compression](https://mlsanity.com/paper/2502.05741/) (2025)
- [Connecting Look and Feel: Associating the visual and tactile properties of physical materials](https://mlsanity.com/paper/1704.03822/) (2017)

## Built on by (most cited first, 2 of 2)

- [OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data](https://mlsanity.com/paper/2602.22286/) (2026)
- [Tac-DINO: Learning Vision-Tactile Features with Patch Alignment](https://mlsanity.com/paper/2606.12069/) (2026)

## Abstract (arXiv)

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.