The researchers say the method allowed them to effectively reproduce six years of walking experience in a single 24-hour period. The data collected in the simulations was then fed into a neural network and loaded on the robot. With the on-board learning, the system can react to its environment in real time and adjust its legs accordingly. The team claims that the system can bring down the cost of robots substantially.
“This system uses vision and feedback from the body directly as input to output commands to the robot’s motors,” researcher Ananye Agarwal said in a post tied to the research. “This technique allows the system to be very robust in the real world. If it slips on stairs, it can recover. It can go into unknown environments and adapt.”
Read more and checkout Carnegie Mellon’s own write up:
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