Picking up a soft 3D object by ``feeling" the grip

Huan Lin, Feng Guo, Feifei Wang, and Yan-Bin Jia


This paper describes a strategy for a robotic hand to pick up deformable 3D objects on a table. Inspired by the human hand behavior, the robotic hand employs two rigid fingers to first squeeze such an object until it ``feels" the object to be liftable. Such ``feeling" is provided by a (virtual) liftability test that is repeatedly conducted during the squeeze. Passing of the test then triggers a lifting action. Throughout the manipulation the object's deformation and its state of contact with the fingers and the table are being tracked based on contact events. Deformable modeling uses the finite element method (FEM) while slip computation employs the homotopy continuation method to determine the contact displacements induced by finger movements. Experiment was conducted over daily items ranging from vegetables to a toy. A simulation-based comparison between deformable grasping and rigid body grasping reveals why soft objects are easier to pick up than hard ones, and demonstrates how a rigid body grasping strategy may fail on soft objects in certain situations.