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 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 robotic 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 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 one 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 minimized human effort. The benchmark, the dataset, and the annotation pipeline are available at https://kitchen-dataset.github.io/KITchen.

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