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：随着机器人在人类生活环境中日益普及，催生了众多物体操作技术的研究，其中灵巧性发挥着至关重要的作用。众所周知，人类在处理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）的深刻理解，以及与之互动时的卓越能力。手部姿态通常受到物体上需要抓取区域的影响，尤其在物体仅部分可见时更为明显。在本研究中，我们通过从局部观测中重建并补全物体的完整几何形状，利用类人物体理解能力，并使用7自由度仿人机械手进行操作。我们的方法将仅依赖部分重建的基线方法抓取成功率显著提升了近30%，并在三类不同物体上实现了超过150次成功抓取。实验证明，该方法在实际场景中能够基于从不同方位和位置补全的物体形状，稳定预测并执行抓取姿态。本研究为提升需要精确抓取与操作真实世界重建物体的机器人应用开辟了新可能。