title: HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose
    Annotations, Affordances, and Reconstructions
summary: We present the HANDAL dataset for category-level object pose estimation and
  affordance prediction. Unlike previous datasets, ours is focused on
  robotics-ready manipulable objects that are of the proper size and shape for
  functional grasping by robot manipulators, such as pliers, utensils, and
  screwdrivers. Our annotation process is streamlined, requiring only a single
  off-the-shelf camera and semi-automated processing, allowing us to produce
  high-quality 3D annotations without crowd-sourcing. The dataset consists of
  308k annotated image frames from 2.2k videos of 212 real-world objects in 17
  categories. We focus on hardware and kitchen tool objects to facilitate
  research in practical scenarios in which a robot manipulator needs to interact
  with the environment beyond simple pushing or indiscriminate grasping. We
  outline the usefulness of our dataset for 6-DoF category-level pose+scale
  estimation and related tasks. We also provide 3D reconstructed meshes of all
  objects, and we outline some of the bottlenecks to be addressed for
  democratizing the collection of datasets like this one.
paper_authors._id: 64cc7257a257a3212c1953b1
paper_authors.name: Andrew Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-04T12:51:12.425Z
paper_authors._id: 645141fb3eb5502631d26dbe
paper_authors.avatarUrl: /avatars/dd963fb21a3112440332c374a5bc8ab3.svg
paper_authors.fullname: Andrew Y. Guo
paper_authors.type: user
paper_authors.user: ykguo
paper_authors._id: 64cc7257a257a3212c1953b2
paper_authors.name: Bowen Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-04T12:51:43.475Z
paper_authors._id: 64aeee717b3b3386662e9e62
paper_authors.avatarUrl: /avatars/8fd244261d6128a109c25ef8578e186b.svg
paper_authors.fullname: Bowen Wen
paper_authors.type: user
paper_authors.user: wenbowen
paper_authors._id: 64cc7257a257a3212c1953b3
paper_authors.name: Jianhe Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-04T12:52:52.353Z
paper_authors._id: 64043d8fb0f2b45ae3b5ba78
paper_authors.avatarUrl: /avatars/f23cd3b112743d4b923bd7a68c23a97e.svg
paper_authors.fullname: Jianhe Yuan
paper_authors.type: user
paper_authors.user: jyuan1118
paper_authors._id: 64cc7257a257a3212c1953b4
paper_authors.name: Jonathan Tremblay
paper_authors._id: 64cc7257a257a3212c1953b5
paper_authors.name: Stephen Tyree
paper_authors._id: 64cc7257a257a3212c1953b6
paper_authors.name: Jeffrey Smith
paper_authors._id: 64cc7257a257a3212c1953b7
paper_authors.name: Stan Birchfield
paper_title: HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose
    Annotations, Affordances, and Reconstructions
paper_summary: We present the HANDAL dataset for category-level object pose estimation and
  affordance prediction. Unlike previous datasets, ours is focused on
  robotics-ready manipulable objects that are of the proper size and shape for
  functional grasping by robot manipulators, such as pliers, utensils, and
  screwdrivers. Our annotation process is streamlined, requiring only a single
  off-the-shelf camera and semi-automated processing, allowing us to produce
  high-quality 3D annotations without crowd-sourcing. The dataset consists of
  308k annotated image frames from 2.2k videos of 212 real-world objects in 17
  categories. We focus on hardware and kitchen tool objects to facilitate
  research in practical scenarios in which a robot manipulator needs to interact
  with the environment beyond simple pushing or indiscriminate grasping. We
  outline the usefulness of our dataset for 6-DoF category-level pose+scale
  estimation and related tasks. We also provide 3D reconstructed meshes of all
  objects, and we outline some of the bottlenecks to be addressed for
  democratizing the collection of datasets like this one.