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We present the HANDAL dataset for category-level object pose estimation andaffordance prediction. Unlike previous datasets, ours is focused onrobotics-ready manipulable objects that are of the proper size and shape forfunctional grasping by robot manipulators, such as pliers, utensils, andscrewdrivers. Our annotation process is streamlined, requiring only a singleoff-the-shelf camera and semi-automated processing, allowing us to producehigh-quality 3D annotations without crowd-sourcing. The dataset consists of308k annotated image frames from 2.2k videos of 212 real-world objects in 17categories. We focus on hardware and kitchen tool objects to facilitateresearch in practical scenarios in which a robot manipulator needs to interactwith the environment beyond simple pushing or indiscriminate grasping. Weoutline the usefulness of our dataset for 6-DoF category-level pose+scaleestimation and related tasks. We also provide 3D reconstructed meshes of allobjects, and we outline some of the bottlenecks to be addressed fordemocratizing the collection of datasets like this one.

HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions