提出一套量化评估视觉触觉传感器性能的框架，解决选型难问题。
TacEva: A Performance Evaluation Framework For Vision-Based Tactile Sensors
- 定义多维度性能指标，设计可复现的实验流程
- 对比多种传感器，揭示不同设计的优劣差异
- 帮助研究者按任务需求选型并优化传感器设计
基于视觉的触觉传感器（VBTS）因高空间分辨率和低成本被广泛应用于机器人任务。然而，其传感机制、结构尺寸等参数差异导致现有传感器性能参差不齐，缺乏标准化评估指标，使得针对特定任务的选型与优化困难重重。为此，本文提出TacEva，一个全面的定量评估框架，定义了涵盖典型应用场景的关键性能指标，并为每个指标设计了结构化实验流程，确保评估的一致性与可重复性。该框架应用于多种具有不同传感机制的VBTS，结果表明其能全面评估各设计性能，并提供各维度的量化指标，使研究者可根据任务需求预先选择最合适的传感器，同时为传感器设计优化提供性能指导。更多现有评估方法及补充评测详见：https://stevenoh2003.github.io/TacEva/
原文摘要 · Abstract (English)
Vision-Based Tactile Sensors (VBTSs) are widely used in robotic tasks because of the high spatial resolution they offer and their relatively low manufacturing costs. However, variations in their sensing mechanisms, structural dimension, and other parameters lead to significant performance disparities between existing VBTSs. This makes it challenging to optimize them for specific tasks, as both the initial choice and subsequent fine-tuning are hindered by the lack of standardized metrics. To address this issue, TacEva is introduced as a comprehensive evaluation framework for the quantitative analysis of VBTS performance. The framework defines a set of performance metrics that capture key characteristics in typical application scenarios. For each metric, a structured experimental pipeline is designed to ensure consistent and repeatable quantification. The framework is applied to multiple VBTSs with distinct sensing mechanisms, and the results demonstrate its ability to provide a thorough evaluation of each design and quantitative indicators for each performance dimension. This enables researchers to pre-select the most appropriate VBTS on a task by task basis, while also offering performance-guided insights into the optimization of VBTS design. A list of existing VBTS evaluation methods and additional evaluations can be found on our website: https://stevenoh2003.github.io/TacEva/
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