We introduce a unified framework for gentle robotic grasping that synergistically couples real-time friction estimation with adaptive grasp control. We propose a new particle filter-based method for real-time estimation of the friction coefficient using vision-based tactile sensors. This estimate is seamlessly integrated into a reactive controller that dynamically modulates grasp force to maintain a stable grip. The two processes operate synchronously in a closed-loop: the controller uses the current best estimate to adjust the force, while new tactile feedback from this action continuously refines the estimation. This creates a highly responsive and robust sensorimotor cycle. The reliability and efficiency of the complete framework are validated through extensive robotic experiments.

翻译：本文提出了一种用于轻柔机器人抓取的统一框架，该框架将实时摩擦估计与自适应抓取控制协同耦合。我们提出了一种新的基于粒子滤波的方法，利用基于视觉的触觉传感器实时估计摩擦系数。该估计值被无缝集成到一个反应式控制器中，该控制器动态调节抓取力以保持稳定抓握。这两个过程在闭环中同步运行：控制器使用当前的最佳估计值来调整力，而由此动作产生的新触觉反馈则持续优化估计。这形成了一个高度响应且鲁棒的感知运动循环。通过广泛的机器人实验验证了该完整框架的可靠性与效率。