Grasp adjustment on novel objects using tactile experience from similar local geometry

Due to pose uncertainty, merely executing a planned-to-be stable grasp usually results in an unstable grasp in the physical world. In our previous work [1], we proposed a tactile experience based grasping pipeline which utilizes tactile feedback to adjust hand posture during the grasping task of known objects and improves the performance of robotic grasping under pose uncertainty. In this paper, we extend our work to grasp novel objects by utilizing local geometric similarity. To do this, we select a series of shape primitives to parameterize potential local geometries which novel objects may share in common. We then build a tactile experience database that stores information of stable grasps on these local geometries. Using this tactile experience database, our method is able to guide a grasp adjustment process to grasp novel objects around similar local geometries. Experiments indicate that our approach improves the grasping performance on novel objects with similar local geometries under pose uncertainty.