PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments

TL;DR

This paper introduces PUGS, a method that quantifies perceptual uncertainty in 3D reconstruction to improve grasp selection robustness in underwater robotic manipulation.

Contribution

It presents a novel framework that propagates occupancy uncertainty into grasp planning, enhancing decision-making under sensory imperfections in underwater environments.

Findings

Uncertainty-aware grasp selection improves robustness against noisy data.

The method performs well in both simulated and real-world underwater scenarios.

Code availability facilitates reproducibility and further research.

Abstract

When navigating and interacting in challenging environments where sensory information is imperfect and incomplete, robots must make decisions that account for these shortcomings. We propose a novel method for quantifying and representing such perceptual uncertainty in 3D reconstruction through occupancy uncertainty estimation. We develop a framework to incorporate it into grasp selection for autonomous manipulation in underwater environments. Instead of treating each measurement equally when deciding which location to grasp from, we present a framework that propagates uncertainty inherent in the multi-view reconstruction process into the grasp selection. We evaluate our method with both simulated and the real world data, showing that by accounting for uncertainty, the grasp selection becomes robust against partial and noisy measurements. Code will be made available at https://onurbagoren.github.io/PUGS/