Google, MIT Partner on Visual Transfer Learning to Help Robots Learn to Grasp, Manipulate Objects

A team from the Massachusetts Institute of Technology (MIT) and Google's artificial intelligence (AI) arm has found a way to use visual transfer learning to help robots grasp and manipulate objects more accurately. "We investigate whether existing pre-trained deep learning visual feature representations can improve the efficiency of learning robotic manipulation tasks, like grasping objects," write Google's Yen-Chen Lin and Andy Zeng of the research. "By studying how we can intelligently transfer neural network weights between vision models and affordance-based manipulation models, we can evaluate how different visual feature representations benefit the exploration process and enable robots to quickly acquire manipulation skills using different grippers. "We initialized our affordance-based manipulation models with backbones based on the ResNet-50 architecture and pre-trained on different vision tasks, including a classification model from ImageNet and a segmentation model from COCO. With different initialisations, the robot was then tasked with learning to grasp a diverse set of objects through trial and error.