Eagles and Algos: Imitation Learning
This is part 3 of a six part series. Links to the other articles in this series can be found at the bottom of this page.
Let us check in on your golf-learning journey. So far, you have had an error-free first attempt at playing a full course, save for the occasional mulligan. All that is left is to complete the final 18th hole. You strike the ball off the tee and make clean contact. It travels 180 yards and lands…in the sand trap! Now this is one scenario you have never encountered in training. Your instructor, without saying a word, grabs your sand wedge, drops another ball right next to yours and shows you a singular demonstration of hitting a ball out of the sand trap. You follow suit with the sand wedge and do the same, perfectly lifting your ball out of the sand trap and onto the putting green, just like she did moments ago. You have just demonstrated imitation learning.
One-shot imitation learning is a facet of the meta-learning framework, applied to robots. It equips robots with the ability to solve new tasks, upon seeing a demonstration of a different but related task. This idea was proposed in the paper, One-Shot Imitation Learning, from a team of researchers at the Berkeley AI Research Lab (BAIR) and the Open AI research lab. While somewhat similar to the model-agnostic meta-learning model, one-shot imitation proposes a neural network that can learn from a single demonstration. The research team demonstrates this proposal by training a Fetch robotic arm (video demonstration below) to stack various number of cube-shaped blocks into predetermined configurations, having observed a single demonstration of block-stacking by a human trainer.