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/

Accurate robotic control over interactions with the environment is fundamentally grounded in understanding tactile contacts. In this paper, we introduce MagicTac, a novel high-resolution grid-based tactile sensor. This sensor employs a 3D multi-layer grid-based design, inspired by the Magic Cube structure. This structure can help increase the spatial resolution of MagicTac to perceive external interaction contacts. Moreover, the sensor is produced using the multi-material additive manufacturing technique, which simplifies the manufacturing process while ensuring repeatability of production. Compared to traditional vision-based tactile sensors, it offers the advantages of i) high spatial resolution, ii) significant affordability, and iii) fabrication-friendly construction that requires minimal assembly skills. We evaluated the proposed MagicTac in the tactile reconstruction task using the deformation field and optical flow. Results indicated that MagicTac could capture fine textures and is sensitive to dynamic contact information. Through the grid-based multi-material additive manufacturing technique, the affordability and productivity of MagicTac can be enhanced with a minimum manufacturing cost of 4.76 GBP and a minimum manufacturing time of 24.6 minutes.