Fast grasping is critical for mobile robots in logistics, manufacturing, and service applications. Existing methods face fundamental challenges in impact stabilization under high-speed motion, real-time whole-body coordination, and generalization across diverse objects and scenarios, limited by fixed bases, simple grippers, or slow tactile response capabilities. We propose \textbf{FastGrasp}, a learning-based framework that integrates grasp guidance, whole-body control, and tactile feedback for mobile fast grasping. Our two-stage reinforcement learning strategy first generates diverse grasp candidates via conditional variational autoencoder conditioned on object point clouds, then executes coordinated movements of mobile base, arm, and hand guided by optimal grasp selection. Tactile sensing enables real-time grasp adjustments to handle impact effects and object variations. Extensive experiments demonstrate superior grasping performance in both simulation and real-world scenarios, achieving robust manipulation across diverse object geometries through effective sim-to-real transfer.

翻译：快速抓取对于物流、制造和服务应用中的移动机器人至关重要。现有方法在高速运动下的冲击稳定性、实时全身协调以及跨多种物体和场景的泛化能力方面面临根本性挑战，这受限于固定基座、简单夹爪或缓慢的触觉响应能力。我们提出\textbf{FastGrasp}，一种基于学习的框架，整合了抓取引导、全身控制和触觉反馈，用于移动快速抓取。我们的两阶段强化学习策略首先通过以物体点云为条件的条件变分自编码器生成多样化的抓取候选，然后在最优抓取选择的引导下执行移动基座、机械臂和手的协调运动。触觉感知实现了实时抓取调整，以应对冲击效应和物体变化。大量实验在仿真和真实场景中均展示了卓越的抓取性能，通过有效的仿真到现实迁移，实现了对不同物体几何形状的鲁棒操作。