Now a new study from researchers at Google has made an important advancement toward robots that can learn to navigate without this help. Within a few hours, relying purely on tweaks to current state-of-the-art algorithms, they successfully got a four-legged robot to learn to walk forward and backward, and turn left and right, completely on its own.
The work builds on previous research conducted a year ago, when the group first figured out how to get the robot to learn in the real world. Reinforcement learning is commonly done in simulation: a virtual doppelgänger of the robot flails around a virtual doppelgänger of the environment until the algorithm is robust enough to operate safely. It is then imported into the physical robot.
They devised a more efficient algorithm that could learn with fewer trials and thus fewer errors, and got the robot up and walking within two hours. Because the physical environment provided natural variation, the robot was also able to quickly adapt to other reasonably similar environments, like inclines, steps, and flat terrain with obstacles.
Source: MIT Tech Review
Name of Author: Karen Hao