Worker robots that learn from mistakes – Science Daily
Excerpt:
Developing the idea of practice makes perfect, the computer scientists at Leeds are bringing together two ideas from AI.
One is automated planning. The robot is able to “see” the problem through a vision system, in effect an image. Software in the robot’s operating system simulates the possible sequence of moves it could make to reach the target object.
But the simulations that have been “rehearsed” by the robot fail to capture the complexity of the real world and when they are implemented, the robot fails to execute the task. For example, it can knock objects off the shelf.
So the Leeds team have combined planning with another AI technique called reinforcement learning.
Reinforcement learning involves the computer in a sequence of trial and error attempts — around 10,000 in total — to reach and move objects. Through these trial and error attempts, the robot “learns” which actions it has planned are more likely to end in success.
The computer undertakes the learning itself, starting off by randomly selecting a planned move that might work. But as the robot learns from trial and error, it becomes more adept at selecting those planned moves that have a greater chance of being successful.
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