立体多阶段空间注意力用于在视觉尺度变化和干扰下的实时移动操作
Stereo Multistage Spatial Attention for Real-Time Mobile Manipulation Under Visual Scale Variation and Disturbances
AI总结：
本文提出一种基于立体多阶段空间注意力的深度预测学习方法，用于实时移动操作，通过层次递归架构整合空间注意力点与机器人状态，提升在视觉尺度变化和干扰下的鲁棒性和任务成功率。
摘要：
在开放、无结构的现实环境中运作的机器人必须依赖机载视觉感知，以自主移动到不同位置。持续变化的机载摄像头视角导致目标物体显著的视觉尺度变化，影响基于视觉的运动生成。本文提出了一种基于立体多阶段空间注意力的深度预测学习方法，用于实时移动操作。所提出的方法从立体图像中提取任务相关的空间注意力点，并通过层次递归架构将它们与机器人状态整合，用于闭环动作预测。我们使用移动机械臂在四个真实世界移动操作任务上评估了该系统，包括刚性放置、关节物体操作和变形物体交互。在随机初始位置和视觉干扰条件下的实验表明，与代表性模仿学习和视觉-语言-动作基线相比，系统在相同控制设置下表现出改进的鲁棒性和任务成功率。结果表明，结构化的立体空间注意力结合预测的时间建模在评估的移动操作场景中提供了一种有效的解决方案。
英文摘要：
Robots operating in open, unstructured real-world environments must rely on onboard visual perception while autonomously moving across different locations. Continuous changes in onboard camera viewpoints cause significant visual scale variations in target objects, affecting vision-based motion generation. In this work, we present a stereo multistage spatial attention-based deep predictive learning method for real-time mobile manipulation. The proposed methods extracts task-relevant spatial attention points from stereo images and integrates them with robot states through a hierarchical recurrent architecture for closed-loop action prediction. We evaluate the system on four real-world mobile manipulation tasks using a mobile manipulator, including rigid placement, articulated object manipulation, and deformable object interaction. Experiments under randomized initial positions and visual disturbance conditions demonstrate improved robustness and task success rates compared to representative imitation learning and vision-language-action baselines under identical control settings. The results indicate that structured stereo spatial attention combined with predictive temporal modeling provides an effective solution within the evaluated mobile manipulation scenarios.
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