LEGS: Learning Efficient Grasp Sets for Exploratory Grasping

TL;DR

LEGS is a novel algorithm that efficiently explores thousands of possible robot grasps by maintaining small promising sets, significantly improving grasp quality identification speed over prior methods in simulation and real-world tests.

Contribution

LEGS introduces an active set-based exploration method for grasping, enabling rapid identification of high-quality grasps on challenging objects, surpassing existing algorithms.

Findings

LEGS outperforms baselines on most objects in simulation datasets.

LEGS converges to high-quality grasps faster in physical experiments.

The method efficiently explores thousands of grasps with fewer exploration steps.

Abstract

While deep learning has enabled significant progress in designing general purpose robot grasping systems, there remain objects which still pose challenges for these systems. Recent work on Exploratory Grasping has formalized the problem of systematically exploring grasps on these adversarial objects and explored a multi-armed bandit model for identifying high-quality grasps on each object stable pose. However, these systems are still limited to exploring a small number or grasps on each object. We present Learned Efficient Grasp Sets (LEGS), an algorithm that efficiently explores thousands of possible grasps by maintaining small active sets of promising grasps and determining when it can stop exploring the object with high confidence. Experiments suggest that LEGS can identify a high-quality grasp more efficiently than prior algorithms which do not use active sets. In simulation experiments, we measure the gap between the success probability of the best grasp identified by LEGS, baselines, and the most-robust grasp (verified ground truth). After 3000 exploration steps, LEGS outperforms baseline algorithms on 10/14 and 25/39 objects on the Dex-Net Adversarial and EGAD! datasets respectively. We then evaluate LEGS in physical experiments; trials on 3 challenging objects suggest that LEGS converges to high-performing grasps significantly faster than baselines. See https://sites.google.com/view/legs-exp-grasping for supplemental material and videos.