  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.
