If you’ve ever played the claw game at an arcade, you know how hard it is to grab and hold onto objects using robotics grippers. Imagine how much more nerve-wracking that game would be if, instead of plush stuffed animals, you were trying to grab a fragile piece of endangered coral or a priceless artifact from a sunken ship.
It's a game a lot of us played as children -- and maybe even later in life: unspooling measuring tape to see how far it would extend before bending. But to engineer, this game was an inspiration, suggesting that measuring tape could become a great material for a robotic gripper.…
While most humans can innately use their hands to communicate with others or grab and manipulate objects, many existing robotic systems only excel at simple manual tasks. In recent years, computer scientists worldwide have been developing machine learning-based models that can process images of humans completing manual tasks, using acquired…
A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.