TacCompress：一个面向灵巧手多点触觉数据压缩的基准
TacCompress: A Benchmark for Multi-Point Tactile Data Compression in Dexterous Hand
AI总结：
本文提出灵巧手多点触觉数据集 Dex-MPTD 与压缩基准 TacCompress，通过图像化触觉数据并评估多种无损、有损编解码器，实现最高约200倍无损与1000倍有损压缩。
摘要：
尽管机器人灵巧操作近年来取得了显著进展，但手部遮挡等挑战仍要求精细的触觉感知，从而促使越来越多的触觉传感器被集成到机器人手中。因此，数据量的增加给手部控制器的信号传输带来了巨大的带宽压力。然而，基于灵巧手物理结构的多点触觉信号采集与压缩尚未得到充分研究。本文的贡献有两方面。首先，我们提出了一个用于灵巧手抓取的多点触觉数据集（Multi-Point Tactile Dataset for Dexterous Hand Grasping，Dex-MPTD）。该数据集采集了多个接触传感器在不同物体和抓取姿态下的触觉信号，为推进灵巧机器人操作研究提供了全面的基准。其次，我们在 Dex-MPTD 上研究无损与有损压缩：将触觉数据转换为图像，并应用六种无损和五种有损图像编解码器以实现高效压缩。实验结果表明，触觉数据可被无损压缩至低至每个子样本 0.0364 比特（bpss），相较原始触觉数据实现约 200 倍压缩比。HM 和 VTM 等高效有损压缩器可在保持可接受数据保真度的同时实现约 1000 倍的数据缩减。有损压缩探索还表明，面向屏幕内容的编码工具在压缩触觉数据方面优于通用编解码器。
英文摘要：
Though robotic dexterous manipulation has progressed substantially recently, challenges like in-hand occlusion still necessitate fine-grained tactile perception, leading to the integration of more tactile sensors into robotic hands. Consequently, the increased data volume imposes substantial bandwidth pressure on signal transmission from the hand's controller. However, the acquisition and compression of multi-point tactile signals based on the dexterous hands' physical structures have not been thoroughly explored. In this paper, our contributions are twofold. First, we introduce a Multi-Point Tactile Dataset for Dexterous Hand Grasping (Dex-MPTD). This dataset captures tactile signals from multiple contact sensors across various objects and grasping poses, offering a comprehensive benchmark for advancing dexterous robotic manipulation research. Second, we investigate both lossless and lossy compression on Dex-MPTD by converting tactile data into images and applying six lossless and five lossy image codecs for efficient compression. Experimental results demonstrate that tactile data can be losslessly compressed to as low as 0.0364 bits per sub-sample (bpss), achieving approximately 200$\times$ compression ratio compared to the raw tactile data. Efficient lossy compressors like HM and VTM can achieve about 1000$\times$ data reductions while preserving acceptable data fidelity. The exploration of lossy compression also reveals that screen-content-targeted coding tools outperform general-purpose codecs in compressing tactile data.
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。
