增强现实用于机器人（ARRO）：通过视觉鲁棒性指向视觉策略
Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness
AI总结：
ARRO通过实时视觉屏蔽和虚拟引导提升机器人操作的视觉鲁棒性，减少对额外数据的依赖，适用于多种桌面任务和真实环境。
摘要：
基于人类专家演示训练的视觉运动策略在广泛机器人操作任务中已显示出强劲的性能。然而，这些策略对背景或机器人本体变化导致的领域偏移仍高度敏感，限制了其泛化能力。在本文中，我们提出了ARRO，一种新的视觉表示方法，利用零样本开放词汇分割和物体检测模型，能够在实时情况下高效地屏蔽掉与任务无关的场景区域，而无需额外训练、建模设置或相机校准。通过在训练和推理过程中过滤视觉干扰并叠加虚拟引导，ARRO提高了对场景变化的鲁棒性，并减少了对额外数据收集的需求。我们广泛评估了ARRO与Diffusion Policy在各种桌面操作任务中的性能，包括仿真和真实环境，并进一步展示了其与通用机器人策略（如Octo、OpenVLA和Pi Zero）的兼容性和有效性。在所有评估设置中，ARRO均显示出一致的性能提升，允许选择性屏蔽以在不同物体之间切换，并在具有挑战性的分割条件下表现出鲁棒性。展示我们结果的视频可在：https://augmented-reality-for-robots.github.io/
英文摘要：
Visuomotor policies trained on human expert demonstrations have recently shown strong performance across a wide range of robotic manipulation tasks. However, these policies remain highly sensitive to domain shifts stemming from background or robot embodiment changes, which limits their generalization capabilities. In this paper, we present ARRO, a novel visual representation that leverages zero-shot open-vocabulary segmentation and object detection models to efficiently mask out task-irrelevant regions of the scene in real time without requiring additional training, modeling of the setup, or camera calibration. By filtering visual distractors and overlaying virtual guides during both training and inference, ARRO improves robustness to scene variations and reduces the need for additional data collection. We extensively evaluate ARRO with Diffusion Policy on a range of tabletop manipulation tasks in both simulation and real-world environments, and further demonstrate its compatibility and effectiveness with generalist robot policies, such as Octo, OpenVLA and Pi Zero. Across all settings in our evaluation, ARRO yields consistent performance gains, allows for selective masking to choose between different objects, and shows robustness even to challenging segmentation conditions. Videos showcasing our results are available at: https://augmented-reality-for-robots.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
