用模块化设计优化触觉传感器，提升机器人感知精度与定制效率。
A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensors
- 将光学系统模块化并参数化，实现可复用的设计框架。
- 通过仿真优化四类目标函数，显著缩短实际调试周期。
- 非专业人员也能借助工具快速完成传感器正向/逆向设计。
GelSight系列基于视觉的触觉传感器在机器人感知与操作任务中表现优异。该传感器通过内部光学系统与嵌入式摄像头捕捉软表面形变，反推接触物体的高分辨率几何信息。然而，为不同机械手定制传感器需反复试错重设计光学系统。本文将设计过程建模为系统性、目标驱动的问题，结合物理精确的光学仿真进行优化。方法基于对光学组件的模块化与参数化，并定义四个通用评价目标函数。我们开发了名为OptiSense Studio的交互式工具箱，使非专家用户能按预设模块和步骤，快速实现正向与逆向设计。通过四个不同型号的GelSight传感器验证，仅在仿真中优化初始设计即可高效迁移到真实设备。
原文摘要 · Abstract (English)
GelSight family of vision-based tactile sensors has proven to be effective for multiple robot perception and manipulation tasks. These sensors are based on an internal optical system and an embedded camera to capture the deformation of the soft sensor surface, inferring the high-resolution geometry of the objects in contact. However, customizing the sensors for different robot hands requires a tedious trial-and-error process to re-design the optical system. In this paper, we formulate the GelSight sensor design process as a systematic and objective-driven design problem and perform the design optimization with a physically accurate optical simulation. The method is based on modularizing and parameterizing the sensor's optical components and designing four generalizable objective functions to evaluate the sensor. We implement the method with an interactive and easy-to-use toolbox called OptiSense Studio. With the toolbox, non-sensor experts can quickly optimize their sensor design in both forward and inverse ways following our predefined modules and steps. We demonstrate our system with four different GelSight sensors by quickly optimizing their initial design in simulation and transferring it to the real sensors.
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