为机器人定制可适配形状与任务的触觉皮肤生成工具
GenTact Toolbox: A Computational Design Pipeline to Procedurally Generate Context-Driven 3D Printed Whole-Body Artificial Skins
- 通过程序化建模贴合机器人外形，实现精准适配
- 基于任务仿真优化传感器布局，提升交互灵敏度
- 支持多材料3D打印，适配不同机器人与应用场景
为机器人开发全身触觉皮肤仍具挑战性，现有方案多采用模块化、通用型设计，虽灵活但难以匹配具体机器人形态和使用场景。本文提出GenTact Toolbox，一个面向机器人形状与应用需求的计算生成管道。方法包括：程序化网格生成以贴合机器人拓扑结构，任务驱动仿真优化传感器分布，以及多材料3D打印实现无形状依赖的制造。我们在Franka Research 3机械臂上部署了六种电容式触觉皮肤，在人机交互场景中验证了该方法的有效性。研究推动触觉传感器从‘通用型’向‘情境驱动’的定制化设计转变，适用于多种机器人系统与应用。项目主页见 https://hiro-group.ronc.one/gentacttoolbox。
原文摘要 · Abstract (English)
Developing whole-body tactile skins for robots remains a challenging task, as existing solutions often prioritize modular, one-size-fits-all designs, which, while versatile, fail to account for the robot's specific shape and the unique demands of its operational context. In this work, we introduce GenTact Toolbox, a computational pipeline for creating versatile whole-body tactile skins tailored to both robot shape and application domain. Our method includes procedural mesh generation for conforming to a robot's topology, task-driven simulation to refine sensor distribution, and multi-material 3D printing for shape-agnostic fabrication. We validate our approach by creating and deploying six capacitive sensing skins on a Franka Research 3 robot arm in a human-robot interaction scenario. This work represents a shift from "one-size-fits-all" tactile sensors toward context-driven, highly adaptable designs that can be customized for a wide range of robotic systems and applications. The project website is available at https://hiro-group.ronc.one/gentacttoolbox
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