The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is well-established that humans exhibit extraordinary dexterity when handling objects. Such dexterity seems to derive from a robust understanding of object properties (such as weight, size, and shape), as well as a remarkable capacity to interact with them. Hand postures commonly demonstrate the influence of specific regions on objects that need to be grasped, especially when objects are partially visible. In this work, we leverage human-like object understanding by reconstructing and completing their full geometry from partial observations, and manipulating them using a 7-DoF anthropomorphic robot hand. Our approach has significantly improved the grasping success rates of baselines with only partial reconstruction by nearly 30% and achieved over 150 successful grasps with three different object categories. This demonstrates our approach's consistent ability to predict and execute grasping postures based on the completed object shapes from various directions and positions in real-world scenarios. Our work opens up new possibilities for enhancing robotic applications that require precise grasping and manipulation skills of real-world reconstructed objects.

翻译：随着机器人在人类环境中的普及，涌现出大量物体操作技术，其中灵巧性起着至关重要的作用。已有研究表明，人类在操作物体时展现出非凡的灵巧性，这种能力似乎源于对物体属性（如重量、尺寸和形状）的稳健理解，以及与物体互动的卓越能力。手部姿态通常反映出需要抓握的物体特定区域的影响，尤其是在物体部分可见时。在本研究中，我们通过从部分观测中重建并补全物体的完整几何形状，并利用7自由度类人机器人手进行操控，从而模拟人类对物体的理解。该方法将仅依赖部分重建的基线方法的抓取成功率提升了近30%，并在三种不同物体类别上实现了超过150次成功抓取。这表明我们的方法能够根据从真实场景中不同方向和位置补全的物体形状，持续预测并执行抓取姿态。本研究为提升需要精确抓取与操控真实重建物体的机器人应用开辟了新可能性。