首个针对触觉数据的压缩基准，评估30种方法在五类任务中的表现。
TaCo: A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data
- 构建首个触觉数据压缩基准TaCo，涵盖多种传感器数据。
- 自研神经压缩模型TaCo-LL（无损）和TaCo-L（有损），性能更优。
- 适用于机器人触觉感知、实时系统开发与压缩算法研究者。
触觉感知对具身智能至关重要，能在复杂环境中提供精细感知与控制。然而，在严格带宽限制下的实时机器人应用中，高效触觉数据压缩仍缺乏充分研究。触觉数据固有的异构性与时空复杂性进一步增加了挑战。为此，我们提出TaCo——首个面向触觉数据编码的综合基准。TaCo评估了30种压缩方法，包括通用压缩算法与神经编码器，覆盖五个来自不同传感器类型的多样化数据集。系统评估无损与有损压缩方案在四项关键任务上的表现：无损存储、人类可视化、材料与物体分类、灵巧机器人抓取。特别地，我们首次提出专为触觉数据训练的数据驱动编码器：TaCo-LL（无损）与TaCo-L（有损）。实验验证了两者优越性能。该基准为理解压缩效率与任务性能之间的权衡提供了基础框架，推动触觉感知技术发展。
原文摘要 · Abstract (English)
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.
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