Regression based robotic grasp detection using Deep learning and Autoencoders

Solving Intelligent object grasping problem in an unstructured environment by a robot manipulator is a challenging task. To grasp an object, the robot should know the position of the object in an environment, decide how and where the hand gripper should be moved and then finally determine how the object is to be held. We have proposed a Hybrid architecture for detecting optimal robotic grasp. The algorithms till now have successfully been applied to RGB images for training and for testing as well. A new hybrid architecture presented in this paper is as follows First, a convolutional neural network (ResNet-50) pre-trained by transfer learning performs regression to grasping rectangles, which will generate multiple rectangles on a single image. Second, an Auto-Encoder would predict quality score for all rectangles regressed by convolutional neural network and choose an optimal rectangle among them. Cornell Grasping Dataset has been used for training and testing purposes but since the dataset is very small, to generalize the model, augmentation has been performed on the images to generate more images. The hybrid architecture gives accuracy of 75.34% on object-wise split and 75.81% on image-wise split.