FunGrasp：面向多样化灵巧手的功能性抓取
FunGrasp: Functional Grasping for Diverse Dexterous Hands
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- ETH Zürich（苏黎世联邦理工学院）
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对灵巧机器人手功能性抓取研究不足的问题，提出FunGrasp系统，通过人到机器人抓取重定向、强化学习训练策略及多种仿真到现实迁移技术，实现不同灵巧手对未见物体的单样本功能性抓取并验证组件有效性。
AI中文摘要：
功能性抓取对人类执行特定任务至关重要，例如通过指孔抓取剪刀以切割材料，或通过刀刃抓取以安全传递。使灵巧机器人手具备功能性抓取能力，对其部署以完成多样化现实任务至关重要。然而，近期灵巧抓取研究常聚焦于强力抓取，却忽视了任务与物体特定的功能性抓取姿态。本文提出FunGrasp系统，可使不同机器人手实现功能性灵巧抓取，并对未见物体进行单样本迁移。给定功能性人类抓取的单张RGBD图像，系统通过人到机器人（H2R）抓取重定向模块估计手部姿态并迁移至不同机器人手。在重定向抓取姿态引导下，通过强化学习在仿真中训练策略以实现动态抓取控制。为实现鲁棒的仿真到现实迁移，采用特权学习、系统辨识、域随机化和重力补偿等技术。实验表明，系统可通过单张RGBD图像对未见物体实现多样化功能性抓取，并成功部署于多种灵巧机器人手。通过全面消融研究验证了各组件的重要性。项目页面：https://hly-123.github.io/FunGrasp/
英文摘要：
Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over. Enabling dexterous robot hands with functional grasping capabilities is crucial for their deployment to accomplish diverse real-world tasks. Recent research in dexterous grasping, however, often focuses on power grasps while overlooking task- and object-specific functional grasping poses. In this paper, we introduce FunGrasp, a system that enables functional dexterous grasping across various robot hands and performs one-shot transfer to unseen objects. Given a single RGBD image of functional human grasping, our system estimates the hand pose and transfers it to different robotic hands via a human-to-robot (H2R) grasp retargeting module. Guided by the retargeted grasping poses, a policy is trained through reinforcement learning in simulation for dynamic grasping control. To achieve robust sim-to-real transfer, we employ several techniques including privileged learning, system identification, domain randomization, and gravity compensation. In our experiments, we demonstrate that our system enables diverse functional grasping of unseen objects using single RGBD images, and can be successfully deployed across various dexterous robot hands. The significance of the components is validated through comprehensive ablation studies. Project page: https://hly-123.github.io/FunGrasp/ .
