让移动机械臂快速精准抓取，靠学习+触觉反馈实现稳准快
FastGrasp: Learning-based Whole-body Control method for Fast Dexterous Grasping with Mobile Manipulators
- 用两阶段强化学习生成多样抓取方案并协调全身动作
- 实测在仿真与真实场景中均实现高鲁棒性抓取，泛化性强
- 适合需要高速抓取的物流、制造等场景的机器人研发者
快速抓取对移动机器人在物流、制造和服务场景中至关重要。现有方法受限于固定基座、简单夹爪或缓慢触觉响应，在高速运动下的冲击稳定、实时全身协调及跨物体与场景泛化方面面临根本挑战。本文提出基于学习的FastGrasp框架，融合抓取引导、全身控制与触觉反馈，实现移动快速抓取。采用两阶段强化学习策略：第一阶段通过条件变分自编码器根据物体点云生成多样抓取候选；第二阶段基于最优抓取选择，协同控制移动基座、机械臂与手部执行动作。触觉传感实现对冲击效应和物体变化的实时调整。大量实验表明，该方法在仿真与真实场景中均表现出色，通过有效的模拟到现实迁移，实现了对多种几何形状物体的鲁棒操作。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。