Fast and Comfortable Interactive Robot-to-Human Object Handover
Chongxi Meng, Tianwei Zhang, Tin Lun Lam
Abstract
Transferring tools and objects to human hands is an important ability of collaborative robots. Most of the existing approaches focus on handover affordance, however, the comfort of receiving objects with human hands is often neglected. In this paper, we use advanced deep learning models to pre-generate handover target configurations that are convenient for human grasping based on the characteristics of the objects and tools, and then the robot grasps and passes the objects to the human. Experimental results on a mobile collaborative robot show that our proposed framework can robustly and efficiently deliver different shapes and types of objects to a human hand of any pose within the robot's field of view in a target pose that is convenient for grasping and can quickly deliver objects to a new target location even after the human hand moves to a new position.