Recently, active vision has reemerged as an important concept for manipulation, since visual occlusion occurs more frequently when main cameras are mounted on the robot heads. We reflect on the visual occlusion issue and identify its essence as the absence of information useful for task completion. Inspired by this, we come up with the more fundamental problem of Exploratory and Focused Manipulation (EFM). The proposed problem is about actively collecting information to complete challenging manipulation tasks that require exploration or focus. As an initial attempt to address this problem, we establish the EFM-10 benchmark that consists of 4 categories of tasks that align with our definition (10 tasks in total). We further come up with a Bimanual Active Perception (BAP) strategy, which leverages one arm to provide active vision and another arm to provide force sensing while manipulating. Based on this idea, we collect a dataset named BAPData for the tasks in EFM-10. With the dataset, we successfully verify the effectiveness of the BAP strategy in an imitation learning manner. We hope that the EFM-10 benchmark along with the BAP strategy can become a cornerstone that facilitates future research towards this direction. Project website: EFManipulation.github.io.

翻译：近年来，主动视觉因其在机器人头部主摄像头视觉遮挡频发的情境下对操作任务的重要性而重新受到关注。本文反思视觉遮挡问题，指出其本质在于缺乏任务完成所需的有效信息。受此启发，我们提出了更具基础性的探索性与聚焦性操作问题。该问题旨在通过主动收集信息以完成需要探索或聚焦的复杂操作任务。作为解决该问题的初步尝试，我们建立了包含4类符合定义任务的EFM-10基准（共计10项任务）。进一步提出双臂主动感知策略，该策略利用单臂提供主动视觉，另一臂在操作过程中提供力觉感知。基于此构想，我们为EFM-10任务构建了名为BAPData的数据集。通过该数据集，我们以模仿学习方式成功验证了BAP策略的有效性。期望EFM-10基准与BAP策略能成为推动该方向未来研究的基石。项目网站：EFManipulation.github.io。