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

• URL: http://arxiv.org/abs/2312.05034v1
• Date: Fri, 8 Dec 2023 13:28:21 GMT
• Title: Grasp Force Optimization as a Bilinear Matrix Inequality Problem: A Deep Learning Approach
• Authors: Hirakjyoti Basumatary, Daksh Adhar, Riddhiman Shaw, and Shyamanta M. Hazarika
• Abstract summary: This paper is to undertake a grasp-mimetic grasping in multifingered hands as a bilinear matrix inequality (BMI) problem. Our analysis is to solve it using a deep approach to make the algorithm efficiently generate force grasps with optimal grasp quality on untrained/unseen objects.
• Score: 0.4999814847776098
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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