Real-time grasp affordance detection of unknown object for robot-human interaction

By using a combination of vision and depth map sensors, this paper aims at detecting the real-time affordability of gripping pose for hand-over object behavior in robot-human interaction. The affordance detection will serve a set of seven-dimension gripping information (3D location, 3D Rotation, and gripping size). Moreover, the novelty is that the goal has to consider gripping behavior of the receiver. Therefore, the result of the proposed method discusses the gripping location of the robot and the estimation of the receivers gripping location. Technically, desired objects detection is computed using a computer vision algorithm. After that, from the depth information, the topological map will be generated using the proposed dynamic density growing neural gas. The density topological structure will be focusing on the desired object. With the topological map information and robot gripper embodiment, the possible gripping position is computed based on the inlier-outlier method. Ranking information in every detected gripping is also considered for classifying from the best and the worst gripping position. Experimental results showed that the proposed work capable of detecting the affordance with a gripping recommendation in real-time with a low computational cost.