Grasping is a fundamental skill for robots to interact with their environment. While grasp execution requires coordinated movement of the hand and arm to achieve a collision-free and secure grip, many grasp synthesis studies address arm and hand motion planning independently, leading to potentially unreachable grasps in practical settings. The challenge of determining integrated arm-hand configurations arises from its computational complexity and high-dimensional nature. We address this challenge by presenting a novel differentiable robot neural distance function. Our approach excels in capturing intricate geometry across various joint configurations while preserving differentiability. This innovative representation proves instrumental in efficiently addressing downstream tasks with stringent contact constraints. Leveraging this, we introduce an adaptive grasp synthesis framework that exploits the full potential of the unified arm-hand system for diverse grasping tasks. Our neural joint space distance function achieves an 84.7% error reduction compared to baseline methods. We validated our approaches on a unified robotic arm-hand system that consists of a 7-DoF robot arm and a 16-DoF multi-fingered robotic hand. Results demonstrate that our approach empowers this high-DoF system to generate and execute various arm-hand grasp configurations that adapt to the size of the target objects while ensuring whole-body movements to be collision-free.

翻译：抓取是机器人与环境交互的基本技能。尽管抓取执行需要手臂与手部的协调运动以实现无碰撞且稳固的抓握，但许多抓取合成研究将手臂与手部的运动规划独立处理，导致在实际环境中可能产生不可达的抓取。确定一体化臂手构型的挑战源于其计算复杂性和高维度特性。我们通过提出一种新颖的可微分机器人神经距离函数来应对这一挑战。该方法能够在保持可微性的同时，精准捕捉多种关节构型下的复杂几何结构。这一创新表示对于高效处理具有严格接触约束的下游任务至关重要。基于此，我们引入了一个自适应抓取合成框架，充分利用统一臂手系统的潜力以完成多样化抓取任务。与基线方法相比，我们的神经关节空间距离函数实现了84.7%的误差缩减。我们在一个由7自由度机器人手臂和16自由度多指灵巧手组成的统一臂手系统上验证了所提方法。结果表明，我们的方法能够使这一高自由度系统生成并执行适应目标物体尺寸的各种臂手抓取构型，同时确保全身运动无碰撞。