Anika Cheerla joins MIT as an undergraduate this fall where she plans to build on her love of math and computer science. A graduate of Monte Vista High School in Cupertino, California, Cheela is interning this summer in HP’s Artificial Intelligence and Emerging Compute Laboratory where she is working on new approaches to teaching robots how to learn. When she’s not studying or working, Cheerla is an accomplished water polo player. She picked MIT despite the fact that they don’t (yet) have a women’s water polo team.
HP: Tell us what you are investigating this summer
I’m working on robust reinforcement learning. Traditional reinforcement learning is a method via which we get robots to learn on their own. But we have to do most of that training in simulations which can result in what we call “overfitting,” where the robot can’t apply what it learned in the simulation to real life because it's trained only on the simulation. I'm working on finding a way to bridge the gap between simulation and real life by making a more general training algorithm that is less tied to any one simulation.
HP: What does that require you to do?
Essentially, I’m varying the simulation through something called curriculum learning, where you slowly change parameters like the amount of friction in your simulation to account for the fact that friction can vary in real life situations. I worked on implementing that on several different robot simulations for the first couple of weeks. Then more recently I’ve been working on my own idea, which I call adversarial environment generation.
