This article investigates the challenge of achieving functional tool-use grasping with high-DoF anthropomorphic hands, with the aim of enabling anthropomorphic hands to perform tasks that require human-like manipulation and tool-use. However, accomplishing human-like grasping in real robots present many challenges, including obtaining diverse functional grasps for a wide variety of objects, handling generalization ability for kinematically diverse robot hands and precisely completing object shapes from a single-view perception. To tackle these challenges, we propose a six-step grasp synthesis algorithm based on fine-grained contact modeling that generates physically plausible and human-like functional grasps for category-level objects with minimal human demonstrations. With the contact-based optimization and learned dense shape correspondence, the proposed algorithm is adaptable to various objects in same category and a board range of robot hand models. To further demonstrate the robustness of the framework, over 10K functional grasps are synthesized to train our neural network, named DexFG-Net, which generates diverse sets of human-like functional grasps based on the reconstructed object model produced by a shape completion module. The proposed framework is extensively validated in simulation and on a real robot platform. Simulation experiments demonstrate that our method outperforms baseline methods by a large margin in terms of grasp functionality and success rate. Real robot experiments show that our method achieved an overall success rate of 79\% and 68\% for tool-use grasp on 3-D printed and real test objects, respectively, using a 5-Finger Schunk Hand. The experimental results indicate a step towards human-like grasping with anthropomorphic hands.

翻译：本文研究了高自由度拟人手实现功能性工具抓取的挑战，旨在使拟人手能够执行需要类人操作和工具使用的任务。然而，在真实机器人中实现类人抓取面临诸多难题，包括为不同物体获取多样化的功能性抓取、处理运动学多样化的机器人手的泛化能力，以及从单视角感知中精确补全物体形状。为应对这些挑战，我们提出了一种基于细粒度接触建模的六步抓取合成算法，该算法通过极少量人类示教即可为类别级物体生成物理合理且类人的功能性抓取。借助基于接触的优化与学习到的密集形状对应关系，所提算法能够适应同一类别中的多种物体以及广泛的机器人手模型。为进一步验证框架的鲁棒性，我们合成了超过1万例功能性抓取数据用于训练神经网络DexFG-Net，该网络基于形状补全模块生成的重建物体模型，可产生多样化的类人功能性抓取。所提框架在仿真与真实机器人平台上均得到充分验证。仿真实验表明，我们的方法在抓取功能性与成功率上大幅优于基线方法；真实机器人实验显示，使用五指Schunk手时，该方法对3D打印物体和真实测试物体的工具抓取成功率分别达到79%和68%。实验结果表明，该方法在实现拟人手类人抓取方面迈出了重要一步。