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Dr Stefanie Tellex, an assistant professor at Brown University who specializes in robot learning and wasn't involved in the study, says the research is 'a big deal' and it could accelerate machine-learning approaches.'It's hard to collect large data sets of robotic data,' Dr Tellex says.'This paper is exciting because it shows that a simulated data set can be used to train a model for grasping.'And this model translates to real successes on a physical robot.'Advances in control algorithms, machine-learning approaches and hardware are building a foundation for a new generation of robots.After doing this, it was successful at lifting objects 99 per cent of the time.The research, conducted in collaboration with a Siemens research group, shows how new approaches to robot learning, combined with the ability for robots to access information through the cloud, could advance robot capabilities, and might enable bots to do useful work in new settings like hospitals and homes.In tests where the bot was more than 50 per cent confident it could grasp an object, it succeeded in lifting the item and shaking it without it dropping it 98 per cent of the time.
The researchers plan to release the 3D data set they created, which could help advance robotics research.Researchers at UC Berkeley have developed a robot that can pick up awkward and unusually shaped objects.The robot learned how to grasp different objects by studying a virtual library of 10,000 3D objects and suitable grasps.The software learns to recognize patterns in digital representations of sounds, images, and other data.The idea that software can simulate the neocortex’s neurons in an artificial 'neural network' is decades old, and it has led to disappointments as breakthroughs.