This work presents a framework for a robot with a multi-fingered hand to freely utilize daily tools, including functional parts like buttons and triggers. An approach heatmap is generated by selecting a functional finger, indicating optimal palm positions on the object's surface that enable the functional finger to contact the tool's functional part. Once the palm position is identified through the heatmap, achieving the functional grasp becomes a straightforward process where the fingers stably grasp the object with low-dimensional inputs using the eigengrasp. As our approach does not need human demonstrations, it can easily adapt to various sizes and designs, extending its applicability to different objects. In our approach, we use directional manipulability to obtain the approach heatmap. In addition, we add two kinds of energy functions, i.e., palm energy and functional energy functions, to realize the eigengrasp. Using this method, each robotic gripper can autonomously identify its optimal workspace for functional grasping, extending its applicability to non-anthropomorphic robotic hands. We show that several daily tools like spray, drill, and remotes can be efficiently used by not only an anthropomorphic Shadow hand but also a non-anthropomorphic Barrett hand.

翻译：本研究提出了一种框架，使配备多指手的机器人能够自由使用日常工具，包括按钮、扳机等功能部件。通过选择功能手指生成趋近热图，该热图标示出物体表面上能使功能手指接触工具功能部件的最优手掌位置。通过热图确定手掌位置后，利用特征抓取的低维输入实现稳定抓握，即可轻松完成功能性抓取。由于本方法无需人类示范，可轻松适应不同尺寸和设计，从而扩展至多种物体的应用场景。本方法采用方向可操作性获取趋近热图，并引入手掌能量与功能能量两种能量函数以实现特征抓取。该方法使每个机械手能自主识别功能性抓取的最优工作空间，从而扩展至非拟人化机械手的应用范畴。实验表明，喷雾器、电钻、遥控器等日常工具不仅能被拟人化的Shadow手高效使用，也能被非拟人化的Barrett手有效操作。