# FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric

## TL;DR

FRoGGeR is a fast, robust grasp synthesis method that uses a novel min-weight metric to generate collision-free grasps in under a second, significantly improving efficiency over existing approaches.

## Contribution

The paper introduces the min-weight metric and demonstrates its effectiveness for rapid, robust grasp generation, enabling real-time applications and refinement of other grasp candidates.

## Key findings

- Typically generates grasps in less than 1 second
- Outperforms baseline in grasp feasibility and success rate
- Effective across diverse object representations

## Abstract

Many approaches to grasp synthesis optimize analytic quality metrics that measure grasp robustness based on finger placements and local surface geometry. However, generating feasible dexterous grasps by optimizing these metrics is slow, often taking minutes. To address this issue, this paper presents FRoGGeR: a method that quickly generates robust precision grasps using the min-weight metric, a novel, almost-everywhere differentiable approximation of the classical epsilon grasp metric. The min-weight metric is simple and interpretable, provides a reasonable measure of grasp robustness, and admits numerically efficient gradients for smooth optimization. We leverage these properties to rapidly synthesize collision-free robust grasps - typically in less than a second. FRoGGeR can refine the candidate grasps generated by other methods (heuristic, data-driven, etc.) and is compatible with many object representations (SDFs, meshes, etc.). We study FRoGGeR's performance on over 40 objects drawn from the YCB dataset, outperforming a competitive baseline in computation time, feasibility rate of grasp synthesis, and picking success in simulation. We conclude that FRoGGeR is fast: it has a median synthesis time of 0.834s over hundreds of experiments.