Grasp Force Optimization as a Bilinear Matrix Inequality Problem: A Deep Learning Approach
This addresses the challenge of efficient and optimal grasp synthesis for robotic manipulation, particularly in biomimetic grasping, though it appears incremental as it applies deep learning to an existing BMI formulation.
The paper tackled the non-convex optimization problem of grasp force synthesis in multi-fingered robotic hands by formulating it as a bilinear matrix inequality (BMI) problem and solving it with a deep learning approach, resulting in an algorithm that efficiently generates force closure grasps with optimal grasp quality on untrained objects.
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.