We present a hybrid learning and model-based approach that adapts internal grasp forces to halt in-hand slip on a multifingered robotic gripper. A multimodal tactile stack combines piezoelectric (PzE) sensing for fast slip cues with piezoresistive (PzR) arrays for contact localization, enabling online construction of the grasp matrix. Upon slip, we update internal forces computed in the null space of the grasp via a quadratic program that preserves the object wrench while enforcing actuation limits. The pipeline yields a theoretical sensing-to-command latency of 35-40 ms, with 5 ms for PzR-based contact and geometry updates and about 4 ms for the quadratic program solve. In controlled trials, slip onset is detected at 20ms. We demonstrate closed-loop stabilization on multifingered grasps under external perturbations. Augmenting efficient analytic force control with learned tactile cues yields both robustness and rapid reactions, as confirmed in our end-to-end evaluation. Measured delays are dominated by the experimental data path rather than actual computation. The analysis outlines a clear route to sub-50 ms closed-loop stabilization.

翻译：本文提出一种混合学习与模型驱动的方法，通过调整内部抓取力来抑制多指机器人夹持器中的手内滑移。多模态触觉传感栈结合压电传感提供的快速滑移信号与压阻阵列实现的接触定位，能够在线构建抓取矩阵。当检测到滑移时，我们通过二次规划更新在抓取零空间中计算得到的内力，该优化在保持物体受力矩的同时强制执行驱动限制。该处理流程的理论传感至指令延迟为35-40毫秒，其中基于压阻的接触与几何更新耗时5毫秒，二次规划求解约需4毫秒。在受控实验中，滑移起始点可在20毫秒内被检测。我们在外部扰动下展示了多指抓取的闭环稳定能力。将高效解析力控制与学习型触觉信号相结合，实现了鲁棒性与快速响应，这在我们端到端的评估中得到了验证。实测延迟主要受实验数据路径而非实际计算影响。分析结果明确了实现低于50毫秒闭环稳定的清晰路径。