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：本工作提出了一种框架，使多指机器人手能够自由使用日常工具，包括按钮和扳机等功能部件。通过选择功能性手指生成接近热图，该热图指示物体表面上的最佳手掌位置，使功能性手指能够接触工具的功能部件。一旦通过热图确定了手掌位置，实现功能性抓取就成为一个直接的过程，即利用特征抓取通过低维输入使手指稳定抓取物体。由于我们的方法不需要人类演示，因此可以轻松适应各种尺寸和设计，将其适用性扩展到不同物体。在方法中，我们使用方向可操控性来获得接近热图。此外，我们添加了两种能量函数，即手掌能量和功能能量函数，以实现特征抓取。使用这种方法，每个机器人抓手可以自主识别其进行功能性抓取的最佳工作空间，从而将其适用性扩展到非拟人化的机器人手中。我们展示了几种日常工具，如喷雾器、电钻和遥控器，不仅可以通过拟人化的Shadow手高效使用，还可以通过非拟人化的Barrett手高效使用。