TaCo: 一种用于异构触觉数据无损和有损编解码器的基准测试
TaCo: A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data
AI总结：
TaCo提出了一种用于评估触觉数据编解码器性能的基准测试，通过评估30种压缩方法，包括神经编解码器，验证了无损和有损压缩方案在多个任务中的优越性能。
摘要：
触觉感知对于具身智能至关重要，为复杂环境中的精细感知和控制提供了支持。然而，高效的触觉数据压缩，这对于在严格带宽限制下实时机器人应用来说至关重要，仍处于探索阶段。触觉数据的固有异质性和时空复杂性进一步加剧了这一挑战。为弥补这一差距，我们引入了TaCo，这是第一个全面的触觉数据编解码器基准测试。TaCo评估了30种压缩方法，包括现成的压缩算法和神经编解码器，在五个不同的数据集上，这些数据集来自各种传感器类型。我们系统地评估了无损和有损压缩方案在四个关键任务上的表现：无损存储、人类可视化、材料和物体分类以及灵巧的机器人抓取。值得注意的是，我们首次开发了数据驱动的编解码器，专门针对触觉数据进行训练，即TaCo-LL（无损）和TaCo-L（有损）。结果验证了我们TaCo-LL和TaCo-L的优越性能。该基准测试为理解压缩效率和任务性能之间的关键权衡提供了基础框架，为未来触觉感知的进步铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
