用触觉反馈实现多指抓握的快速防滑，响应快至30毫秒。
Reactive Slip Control in Multifingered Grasping: Hybrid Tactile Sensing and Internal-Force Optimization
- 融合学习型触觉滑动检测与模型化内力控制，实时防滑
- 滑动检测延迟仅20.4±6毫秒，整体响应低于50毫秒
- 无需摩擦模型或力传感器，适合真实机器人抓取场景
我们构建了一个由快速触觉反馈驱动的低层反射控制层，用于多指抓握的稳定。该混合方法结合了学习型触觉滑动检测与基于模型的内力控制，在保持物体级力矩的同时阻止手内滑动。多模态触觉堆叠集成了压电传感（PzE）以获取快速滑动信号和压阻阵列（PzR）以实现接触定位，可在无先验物体知识条件下在线构建以接触为中心的抓握表示。实验表明，在外部扰动下可实现多指抓握的反应式稳定，无需显式摩擦模型或直接力传感。在受控测试中，滑动起始检测时间仅为20.4 ± 6毫秒。该框架理论上的抓握响应延迟约为30毫秒，抓握模型更新小于5毫秒，内力选择约需4毫秒。分析支持了亚50毫秒触觉驱动抓握响应的可行性，与人类反射基线一致。
原文摘要 · Abstract (English)
We build a low-level reflex control layer driven by fast tactile feedback for multifinger grasp stabilization. Our hybrid approach combines learned tactile slip detection with model-based internal-force control to halt in-hand slip while preserving the object-level wrench. The multimodal tactile stack integrates piezoelectric sensing (PzE) for fast slip cues and piezoresistive arrays (PzR) for contact localization, enabling online construction of a contact-centric grasp representation without prior object knowledge. Experiments demonstrate reactive stabilization of multifingered grasps under external perturbations, without explicit friction models or direct force sensing. In controlled trials, slip onset is detected after 20.4 +/- 6 ms. The framework yields a theoretical grasp response latency on the order of 30 ms, with grasp-model updates in less than 5 ms and internal-force selection in about 4 ms. The analysis supports the feasibility of sub-50 ms tactile-driven grasp responses, aligned with human reflex baselines.
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