This paper addresses the multi-faceted problem of robot grasping, where multiple criteria may conflict and differ in importance. We introduce Grasp Ranking and Criteria Evaluation (GRaCE), a novel approach that employs hierarchical rule-based logic and a rank-preserving utility function to optimize grasps based on various criteria such as stability, kinematic constraints, and goal-oriented functionalities. Additionally, we propose GRaCE-OPT, a hybrid optimization strategy that combines gradient-based and gradient-free methods to effectively navigate the complex, non-convex utility function. Experimental results in both simulated and real-world scenarios show that GRaCE requires fewer samples to achieve comparable or superior performance relative to existing methods. The modular architecture of GRaCE allows for easy customization and adaptation to specific application needs.

翻译：本文针对机器人抓取中多准则相互冲突且重要性各异的多维度问题，提出了一种名为抓取排序与准则评估（GRaCE）的新方法。该方法采用基于层次规则的逻辑与保秩效用函数，根据稳定性、运动学约束及目标导向功能等多种准则优化抓取方案。此外，我们提出了混合优化策略GRaCE-OPT，该策略融合了基于梯度与无梯度方法，有效应对复杂的非凸效用函数。仿真与真实场景实验结果表明，相较于现有方法，GRaCE能以更少的采样数达到同等或更优性能。GRaCE的模块化架构便于根据特定应用需求进行定制与适配。