As a rule, robots have to learn through explicit instruction, whether it’s through new programming, watching videos or holding their hands. UC Berkeley’s BRETT (Berkeley Robot for the Elimination of Tedious Tasks) isn’t nearly that dependent, however. The machine uses neural network-based deep learning algorithms to master tasks through trial and error, much like humans do. Ask it to assemble a toy and it’ll keep trying until it understands what works. In theory, you’d rarely need to give the robot new code — you’d just make requests and give the automaton enough time to figure things out.
Robot learns skills through trial and error, like you do — by Jon Fingas Engadget
Posted in robotics/AI