HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose
  Annotations, Affordances, and Reconstructions
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.
64cc7257a257a3212c1953b1
Andrew Guo
admin_assigned
2023-08-04T12:51:12.425Z
645141fb3eb5502631d26dbe
/avatars/dd963fb21a3112440332c374a5bc8ab3.svg
Andrew Y. Guo
user
ykguo
64cc7257a257a3212c1953b2
Bowen Wen
admin_assigned
2023-08-04T12:51:43.475Z
64aeee717b3b3386662e9e62
/avatars/8fd244261d6128a109c25ef8578e186b.svg
Bowen Wen
user
wenbowen
64cc7257a257a3212c1953b3
Jianhe Yuan
admin_assigned
2023-08-04T12:52:52.353Z
64043d8fb0f2b45ae3b5ba78
/avatars/f23cd3b112743d4b923bd7a68c23a97e.svg
Jianhe Yuan
user
jianhey
64cc7257a257a3212c1953b4
Jonathan Tremblay
64cc7257a257a3212c1953b5
Stephen Tyree
64cc7257a257a3212c1953b6
Jeffrey Smith
64cc7257a257a3212c1953b7
Stan Birchfield
HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose
  Annotations, Affordances, and Reconstructions
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.
The HANDAL dataset provides 3D annotations of robotics-ready objects for 6-DoF pose estimation and affordance prediction, enhancing real-world robot interaction scenarios.
category-level object pose estimation affordance prediction robotics-ready manipulable objects 6-DoF pose+scale estimation