GraspXL: Generating Grasping Motions for Diverse Objects at Scale
Hui Zhang, Sammy Christen, Zicong Fan, Otmar Hilliges, Jie Song

TL;DR
GraspXL is a scalable policy learning framework that synthesizes diverse, high-quality grasping motions for a wide range of objects without requiring expensive 3D hand-object interaction data.
Contribution
It unifies multiple grasping objectives across diverse objects and hand morphologies, enabling robust motion generation without 3D interaction data, and scales to over 500k unseen objects.
Findings
Achieves 82.2% success rate on unseen objects
Generates diverse grasps per object
Works with different dexterous hands
Abstract
Human hands possess the dexterity to interact with diverse objects such as grasping specific parts of the objects and/or approaching them from desired directions. More importantly, humans can grasp objects of any shape without object-specific skills. Recent works synthesize grasping motions following single objectives such as a desired approach heading direction or a grasping area. Moreover, they usually rely on expensive 3D hand-object data during training and inference, which limits their capability to synthesize grasping motions for unseen objects at scale. In this paper, we unify the generation of hand-object grasping motions across multiple motion objectives, diverse object shapes and dexterous hand morphologies in a policy learning framework GraspXL. The objectives are composed of the graspable area, heading direction during approach, wrist rotation, and hand position. Without…
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Taxonomy
TopicsRobot Manipulation and Learning · Human Pose and Action Recognition · Human Motion and Animation
