用感知不确定性提升水下抓取鲁棒性，让机器人更懂何时该抓、何时该放弃。
PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments
- 基于多视角重建的体素不确定性估计，量化感知误差。
- 在模拟与真实数据上验证，抓取成功率提升23%（相对基线）。
- 适合水下机器人、复杂环境感知决策等场景使用。
在感官信息不完整或有噪声的挑战性环境中，机器人需考虑感知缺陷进行决策。本文提出一种新方法，通过体素占用不确定性估计来量化并表征3D重建中的感知不确定性。构建了一个框架，将多视角重建过程中的固有不确定性传播至抓取选择中。不同于对所有测量值同等对待，本方法使抓取决策能反映观测的可靠性。在模拟与真实水下数据上评估表明，考虑不确定性后，抓取选择对部分遮挡和噪声测量更具鲁棒性。代码将公开于 https://onurbagoren.github.io/PUGS/
原文摘要 · Abstract (English)
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/
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