Humans are highly skilled in communicating their intent for when and where a handover
would occur. However, even the state-of-the-art robotic implementations for handovers
typically lack of such communication skills. This study investigates visualization of the robot's
internal state and intent for Human-to-Robot Handovers using Augmented Reality.
Specifically, we explore the use of visualized 3D models of the object and the robotic gripper
to communicate the robot's estimation of where the object is and the pose in which the robot …