This paper addresses the multi-faceted problem of robot grasping, where multiple criteria may conflict and differ in importance. We introduce a probabilistic framework, Grasp Ranking and Criteria Evaluation (GRaCE), which employs hierarchical rule-based logic and a rank-preserving utility function for grasps based on various criteria such as stability, kinematic constraints, and goal-oriented functionalities. GRaCE's probabilistic nature means the framework handles uncertainty in a principled manner, i.e., the method is able to leverage the probability that a given criteria is satisfied. 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：本文探讨机器人抓取这一多层面问题，其中多个评判标准可能相互冲突且重要性各异。我们提出一种概率框架——抓取排序与标准评估（GRaCE），该框架采用基于层次化规则的逻辑，并构建了基于稳定性、运动学约束及面向目标的功能性等多重标准的抓取排序保持效用函数。GRaCE的概率特性意味着框架能以原则性方式处理不确定性，即该方法能够利用特定标准被满足的概率。此外，我们提出GRaCE-OPT混合优化策略，结合基于梯度与无梯度的优化方法，以有效探索复杂非凸的效用函数空间。在仿真与真实场景中的实验结果表明，相较于现有方法，GRaCE只需更少的样本即可达到相当或更优的性能。GRaCE的模块化架构便于根据具体应用需求进行定制化调整与适配。