In many realistic settings, a robot is tasked with grasping an object without knowing its exact pose. Instead, the robot relies on a probabilistic estimation of the pose to decide how to attempt the grasp. We offer a novel Value of Assistance (VOA) measure for assessing the expected effect a specific observation will have on the robot's ability to successfully complete the grasp. Thus, VOA supports the decision of which sensing action would be most beneficial to the grasping task. We evaluate our suggested measures in both simulated and real-world robotic settings.

翻译：在许多现实场景中，机器人需要在未知物体精确位姿的情况下完成抓取任务。机器人依赖对位姿的概率估计来决定抓取策略。我们提出了一种新颖的辅助价值（VOA）度量，用于评估特定观测对机器人成功完成抓取任务能力的预期影响。因此，VOA能够辅助判断哪种感知行动对抓取任务最为有利。我们分别在模拟环境和真实机器人场景中评估了所提出的度量方法。