Grasp classification system improves human-to-robot handovers

Giving and taking objects to and from humans are fundamental capabilities for collaborative robots in a variety of applications. NVIDIA researchers are hoping to improve these human-to-robot handovers by thinking about them as a hand grasp classification problem. In a paper called "Human Grasp Classification for Reactive Human-to-Robot Handovers", researchers at NVIDIA's Seattle AI Robotics Research Lab describe a proof of concept they claim results in more fluent human-to-robot handovers compared to previous approaches. The system classifies a human's grasp and plans a robot's trajectory to take the object from the human's hand. To do this, the researchers developed a perception system that can accurately identify a hand and objects in a variety of poses. This was no easy task, they said, as often times the hand and object are obstructed by each other.