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

arXiv: https://arxiv.org/abs/2312.05034 · HTML: https://mlsanity.com/paper/2312.05034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics](https://mlsanity.com/paper/1908.05209/) (2019)

## Abstract (arXiv)

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.