kpam-sc: Generalizable manipulation planning using keypoint affordance and shape completion
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021•ieeexplore.ieee.org
While traditional approaches to manipulation planning assume known object templates,
recent approaches to" category-level manipulation" aim to manipulate a category of objects
with potentially unknown instances and large intra-category shape variation. In this paper
we explore an object representation to enable precise category-level manipulation,
capturing a notion of the object configuration and extent, while being generalizable to novel
instances. Building on our previous work, kPAM 1, we combine semantic keypoints with …
recent approaches to" category-level manipulation" aim to manipulate a category of objects
with potentially unknown instances and large intra-category shape variation. In this paper
we explore an object representation to enable precise category-level manipulation,
capturing a notion of the object configuration and extent, while being generalizable to novel
instances. Building on our previous work, kPAM 1, we combine semantic keypoints with …
While traditional approaches to manipulation planning assume known object templates, recent approaches to "category-level manipulation" aim to manipulate a category of objects with potentially unknown instances and large intra-category shape variation. In this paper we explore an object representation to enable precise category-level manipulation, capturing a notion of the object configuration and extent, while being generalizable to novel instances. Building on our previous work, kPAM 1 , we combine semantic keypoints with dense geometry (a point cloud or mesh) as the interface between the perception module and motion planner. Leveraging advances in learning-based keypoint detection and shape completion, both dense geometry and keypoints can be perceived from raw sensor input. Using the proposed hybrid object representation, we formulate the manipulation task as a motion planning problem which encodes both the object target configuration and physical feasibility for a category of objects. In this way, many existing manipulation planners can be generalized to categories of objects, and the resulting perception-to-action manipulation pipeline is robust to large intra-category shape variation. Extensive hardware experiments demonstrate our pipeline can produce robot trajectories that accomplish tasks with never-before-seen objects. The video demo is available on this link: https://sites.google.com/view/generalizable-manipulation.
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