Grasp force synthesis is a non-convex optimization problem involving constraints that are bilinear. Traditional approaches to this problem involve general-purpose gradient-based nonlinear optimization and semi-definite programming. With a view towards dealing with postural synergies and non-smooth but convex positive semidefinite constraints, we look beyond gradient-based optimization. The focus of this paper is to undertake a grasp analysis of biomimetic grasping in multi-fingered robotic hands as a bilinear matrix inequality (BMI) problem. Our analysis is to solve it using a deep learning approach to make the algorithm efficiently generate force closure grasps with optimal grasp quality on untrained/unseen objects.

翻译：抓取力合成是一个非凸优化问题，其约束条件呈双线性特性。传统方法通常采用通用梯度式非线性优化与半定规划进行求解。为应对姿态协同效应及非光滑但凸的正半定约束，我们突破梯度优化的局限。本文聚焦于将仿生抓取分析——多指机器人手中的仿生抓取——建模为双线性矩阵不等式（BMI）问题，并采用深度学习方法进行求解，以使算法能够针对未训练/未见物体高效生成具有最优抓取质量的力闭合抓取。