DexSkin：用于学习接触丰富操作的高覆盖顺应性机器人皮肤
DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation
AI总结：
本文提出DexSkin，一种柔软可定制的电容式电子皮肤，通过为平行夹爪手指提供全覆盖触觉感知，结合从演示学习和在线强化学习，验证了其在接触丰富操作任务中的有效性及跨传感器模型迁移能力。
摘要：
人类皮肤提供了丰富的触觉感知流，能够在大面积且具有轮廓的区域上定位有意和无意的接触事件。在灵巧机器人操作系统中复现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，我们通过引入DexSkin向这一目标迈出了一步。DexSkin是一种柔软、顺应性的电容式电子皮肤，能够实现灵敏、可定位且可校准的触觉感知，并且可以根据不同的几何形状进行定制。我们通过为一对平行夹爪手指加装传感器来展示其在学习下游机器人操作方面的有效性，提供了几乎覆盖整个手指表面的触觉覆盖。我们在从演示中学习的框架下，实证评估了DexSkin在学习具有挑战性的操作任务中的能力，这些任务需要覆盖手指整个表面的感知，例如在手中重新定向物体以及将弹性带缠绕在盒子上。随后，我们展示了对于数据驱动方法至关重要的特性：DexSkin可以被校准以实现跨传感器实例的模型迁移，并展示了其在真实机器人上在线强化学习的适用性。我们的结果突显了DexSkin在学习真实世界接触丰富操作方面的适用性和实用性。请访问我们的项目网页查看视频和可视化内容：https://dex-skin.github.io/。
英文摘要：
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: https://dex-skin.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
