Despite the recent progress on 6D object pose estimation methods for robotic grasping, a substantial performance gap persists between the capabilities of these methods on existing datasets and their efficacy in real-world grasping and mobile manipulation tasks, particularly when robots rely solely on their monocular egocentric field of view (FOV). Existing real-world datasets primarily focus on table-top grasping scenarios, where a robot arm is placed in a fixed position and the objects are centralized within the FOV of fixed external camera(s). Assessing performance on such datasets may not accurately reflect the challenges encountered in everyday grasping and mobile manipulation tasks within kitchen environments such as retrieving objects from higher shelves, sinks, dishwashers, ovens, refrigerators, or microwaves. To address this gap, we present KITchen, a novel benchmark designed specifically for estimating the 6D poses of objects located in diverse positions within kitchen settings. For this purpose, we recorded a comprehensive dataset comprising around 205k real-world RGBD images for 111 kitchen objects captured in two distinct kitchens, utilizing a humanoid robot with its egocentric perspectives. Subsequently, we developed a semi-automated annotation pipeline, to streamline the labeling process of such datasets, resulting in the generation of 2D object labels, 2D object segmentation masks, and 6D object poses with minimal human effort. The benchmark, the dataset, and the annotation pipeline will be publicly available at https://kitchen-dataset.github.io/KITchen.

翻译：尽管近期在机器人抓取的6D物体姿态估计方法上取得了进展，但现有数据集上这些方法的性能与它们在真实世界抓取及移动操作任务中的有效性之间仍存在显著差距，尤其是在机器人仅依赖其单目自我中心视野（FOV）的情况下。现有的真实世界数据集主要集中于桌面抓取场景，其中机械臂被固定放置，且物体集中在外置固定摄像头的FOV内。在此类数据集上评估性能可能无法准确反映厨房环境中日常抓取和移动操作任务所面临的挑战，例如从较高货架、水槽、洗碗机、烤箱、冰箱或微波炉中取放物体。为弥补这一空白，我们提出了KITchen，这是一个专门为估计厨房场景中不同位置物体的6D姿态而设计的新基准。为此，我们利用人形机器人及其自我中心视角，在两个不同的厨房中记录了包含111个厨房物体约20.5万张真实世界RGBD图像的综合性数据集。随后，我们开发了一个半自动标注流程，以简化此类数据集的标注过程，从而以最少的人工投入生成了2D物体标签、2D物体分割掩码和6D物体姿态。该基准、数据集及标注流程将在https://kitchen-dataset.github.io/KITchen 公开提供。