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arXiv · 2312.05034

Grasp Force Optimization as a Bilinear Matrix Inequality Problem: A Deep Learning Approach

Hirakjyoti Basumatary·Daksh Adhar·Riddhiman Shaw·Shyamanta M. Hazarika
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Abstract

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.

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Hirakjyoti Basumatary, Daksh Adhar, Riddhiman Shaw, Shyamanta M. Hazarika. 2023-12-08. Grasp Force Optimization as a Bilinear Matrix Inequality Problem: A Deep Learning Approach. https://arxiv.org/abs/2312.05034

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