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 \href{https://roamlab.github.io/spikeatac/}{roamlab.github.io/spikeatac}.

翻译：在本工作中，我们介绍了SpikeATac，一种多模态触觉手指，它将像素化且高灵敏度的动态响应（PVDF）与静态传感方法（电容式）相结合，实现多模态触觉感知。SpikeATac因其‘尖峰’响应而得名，其16像素的PVDF薄膜以4 kHz采样，为接触的起始和中断提供快速、灵敏的动态信号。我们表征了不同模态的灵敏度，并表明SpikeATac在抓取易碎、可变形物体时能够快速而轻柔地停止。除了平行抓取，我们还展示了SpikeATac可用于基于学习的框架，在多指灵巧机器人手上实现新能力。我们采用一种结合人类反馈强化学习与基于触觉奖励的学习方法，来微调策略以调节力的行为。我们的硬件平台和学习流程共同实现了一项先前未达成的困难灵巧且接触密集的任务：易碎物体的手内操作。视频可在 \href{https://roamlab.github.io/spikeatac/}{roamlab.github.io/spikeatac} 查看。