实时估算摩擦系数并自适应调节抓力，实现稳定轻柔抓取
Synchronized Online Friction Estimation and Adaptive Grasp Control for Robust Gentle Grasp
- 用视觉触觉传感器结合粒子滤波实时估算摩擦系数
- 抓力根据摩擦估计动态调整，保持稳定抓握
- 适合需要精细操作的机器人抓取任务
我们提出一种统一框架，实现稳健的轻柔机器人抓取，将实时摩擦系数估计与自适应抓取控制协同融合。提出一种基于粒子滤波的新方法，利用视觉触觉传感器实现实时摩擦系数估计。该估计结果无缝集成到反应式控制器中，动态调节抓取力以维持稳定握持。两个过程在闭环中同步运行：控制器使用当前最优估计值调整抓力，而此次动作产生的新触觉反馈持续优化估计。这形成了高度响应且鲁棒的感知-运动循环。通过大量机器人实验验证了该完整框架的可靠性与高效性。
原文摘要 · Abstract (English)
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.
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