In this work, we introduce SpikeATac, a multimodal tactile finger combining a taxelized and highly sensitive dynamic response (PVDF) with a static transduction method (capacitive) for multimodal touch sensing. Named for its `spiky' response, SpikeATac's 16-taxel PVDF film sampled at 4 kHz provides fast, sensitive dynamic signals to the very onset and breaking of contact. We characterize the sensitivity of the different modalities, and show that SpikeATac provides the ability to stop quickly and delicately when grasping fragile, deformable objects. Beyond parallel grasping, we show that SpikeATac can be used in a learning-based framework to achieve new capabilities on a dexterous multifingered robot hand. We use a learning recipe that combines reinforcement learning from human feedback with tactile-based rewards to fine-tune the behavior of a policy to modulate force. Our hardware platform and learning pipeline together enable a difficult dexterous and contact-rich task that has not previously been achieved: in-hand manipulation of fragile objects. Videos are available at https://roamlab.github.io/spikeatac/ .

翻译：本研究提出SpikeATac——一种多模态触觉手指，它将像素化高灵敏度动态响应（PVDF）与静态传感方法（电容式）相结合，实现多模态触觉感知。该手指因其"尖峰状"响应特性而得名，其16像素点的PVDF薄膜以4 kHz频率采样，能够对接触的起始与中断提供快速、灵敏的动态信号。我们对不同模态的灵敏度进行了表征，结果表明SpikeATac在抓取易碎可变形物体时能够实现快速而轻柔的停止动作。除平行抓取外，我们进一步证明SpikeATac可在基于学习的框架中应用于灵巧多指机器人手，以实现新功能。我们采用一种结合人类反馈强化学习与触觉奖励的学习方案，对力调制策略的行为进行微调。我们的硬件平台与学习流程共同实现了一项此前尚未达成的、高难度且接触密集的灵巧操作任务：易碎物体的手内操控。演示视频详见 https://roamlab.github.io/spikeatac/ 。