一款能快速感知触觉变化的多模态机械手指，适合精细抓取易碎物品。
SpikeATac: A Multimodal Tactile Finger with Taxelized Dynamic Sensing for Dexterous Manipulation
- 融合压电与电容传感，16个触点每秒采样4000次，响应极快。
- 在抓取易碎物时可精准控制力度，实现快速停顿和轻柔操作。
- 配合强化学习，让机器人手完成此前无法实现的翻转操作。
本文提出SpikeATac，一种结合压电（PVDF）动态传感与电容静态传感的多模态触觉手指。其16个触点以4 kHz频率采样，可捕捉接触开始与分离的瞬时变化，具备高灵敏度。实验表明，该系统在抓握脆弱、可变形物体时能快速、精准地调节力矩。此外，在基于人类反馈强化学习的框架下，该系统使多指灵巧机械手实现了前所未有的在手操控能力，成功完成对易碎物体的精细操作。视频演示见https://roamlab.github.io/spikeatac/。
原文摘要 · Abstract (English)
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 reinforcement learning from human feedback 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/ .
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