Abstract
Human skin provides a rich tactile sensing stream, localizing intentional and unintentional contact events over a large and contoured region. Replicating these tactile sensing capabilities for dexterous robotic manipulation systems remains a longstanding challenge. In this work, we take a step towards this goal by introducing DexSkin. DexSkin is a soft, conformable capacitive electronic skin that enables sensitive, localized, and calibratable tactile sensing, and can be tailored to varying geometries. We demonstrate its efficacy for learning downstream robotic manipulation by sensorizing a pair of parallel jaw gripper fingers, providing tactile coverage across almost the entire finger surfaces. We empirically evaluate DexSkin's capabilities in learning challenging manipulation tasks that require sensing coverage across the entire surface of the fingers, such as reorienting objects in hand and wrapping elastic bands around boxes, in a learning-from-demonstration framework. We then show that, critically for data-driven approaches, DexSkin can be calibrated to enable model transfer across sensor instances, and demonstrate its applicability to online reinforcement learning on real robots. Our results highlight DexSkin's suitability and practicality for learning real-world, contact-rich manipulation. Please see our project webpage for videos and visualizations: this https URL.
Abstract (translated)
人类皮肤提供了丰富的触觉感知流，能够精确定位在大范围且轮廓复杂的区域内的有意或无意接触事件。为灵巧的机器人操作系统复制这些触觉感知能力仍然是一个长期存在的挑战。在这项工作中，我们朝着这一目标迈出了第一步，通过引入DexSkin来实现这一目标。DexSkin是一种柔软、可贴合的电容式电子皮肤，能够提供敏感、局部化且可以校准的触觉感应，并可根据不同几何形状进行定制。 我们在学习下游机器人操作中展示了DexSkin的有效性，例如在一对平行夹爪指头上安装传感器，覆盖几乎整个手指表面。我们通过实证评估了DexSkin在处理需要全表面感知覆盖度的挑战性操作任务方面的能力，比如重新定向手中的物体和将弹性带缠绕在盒子上，这些都在一个基于示范学习的框架中进行。 然后，我们展示了对于数据驱动方法而言至关重要的事实——DexSkin可以被校准以实现在不同传感器实例之间的模型迁移，并演示了其在线强化学习在真实机器人上的适用性。我们的结果显示了DexSkin在学习现实世界接触密集型操作任务中的适宜性和实用性。 有关视频和可视化展示，请参见项目网页：[此链接](http://example.com)。（请将"http URL"替换为实际的网址）
URL
https://arxiv.org/abs/2509.18830
