Grasp Force Optimization As A Bilinear Matrix Inequality Problem A Deep Learning Approach

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

**arXiv ID:** 2312.05034
**Authors:** Hirakjyoti Basumatary, Daksh Adhar, Riddhiman Shaw, Shyamanta M. Hazarika
**Published:** 2023-12-08T13:28:21Z
**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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## References

- [arXiv:2312.05034](http://arxiv.org/abs/2312.05034v1)