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.

翻译：触觉传感对于具身智能至关重要，其在复杂环境中提供细粒度的感知与控制。然而，高效的触觉数据压缩对于严格带宽约束下的实时机器人应用极为关键，目前仍研究不足。触觉数据固有的异构性及时空复杂性进一步加剧了这一挑战。为填补这一空白，我们提出了首个全面的触觉数据编解码器基准测试——TaCo。TaCo 在来自不同传感器类型的五个多样化数据集上，评估了包括现成压缩算法与神经编解码器在内的 30 种压缩方法。我们系统性地评估了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、材料与物体分类以及灵巧机器人抓取。值得注意的是，我们率先开发了基于触觉数据显式训练的数据驱动编解码器——TaCo-LL（无损）与 TaCo-L（有损）。实验结果验证了我们的 TaCo-LL 与 TaCo-L 的卓越性能。该基准测试为理解压缩效率与任务性能之间的关键权衡提供了基础框架，为触觉感知的未来发展铺平了道路。