Researchers at the Italian Institute of Technology (IIT) have recently been exploring a fascinating idea, that of creating humanoid robots that can fly. To efficiently control the movements of flying robots, objects or vehicles, however, researchers require systems that can reliably estimate the intensity of the thrust produced by propellers, which allow them to move through the air. As thrust forces are difficult to measure directly, they are usually estimated based on data collected by onboard sensors. The team at IIT recently introduced a new framework that can estimate thrust intensities of flying multibody systems that are not equipped with thrust-measuring sensors. This framework, presented in a paper published in IEEE Robotics and Automation Letters, could ultimately help them to realize their envisioned flying humanoid robot.

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.

OpenAI, an Elon Musk-backed AI research company, developed a robotic hand that is able to teach itself how to manipulate objects with a human-like dexterity. For more videos, subscribe to Mashable Daily: http://on.mash.to/SubscribeNews Give us a follow: Facebook: https://www.facebook.com/mashable/

Robots are learning how to use tails in all sorts of different ways. U.C. Berkeley had that brilliant idea of using an active tail to control the orientation of a robot in mid air, and that basic idea has expanded to running robots with and even robotic cars looking for hyper maneuverability. The thing that all of these robots have in common with each other, and with animals, is that their tails are actuated: in order to function, they depend on motors to get them to move around and do stuff. And of course they're actuated, because what use would they be if you couldn't control them? Young-Ho Kim and Dylan A. Shell from Texas A&M University recently published a paper in IEEE Robotics and Automation Letters on "Using a Compliant, Unactuated Tail to Manipulate Objects."