Scientists at Carnegie Mellon University and the University of California, Berkeley, have enabled a low-cost and relatively small legged robot to adapt to obstacles. The robot uses its vision and an onboard computer to quickly adjust to new situations and master difficult terrain. The researchers trained it using 4,000 robot clones as they walked and climbed in a simulator, giving the machine six years of experience in one day.
The simulator also retained motor skills acquired in training in a neural network that the team copied to the actual robot. The team put the robot through its paces, testing it on uneven stairs and hillsides at public parks, challenging it to walk across steppingstones and over slippery surfaces, and asking it to climb stairs that, for its height, would be akin to a human leaping over a hurdle. The robot adapts quickly and masters challenging terrain by relying on its vision and a small onboard computer.
More information:
https://www.cmu.edu/news/stories/archives/2022/november/visual-locomotion.html