Welcome to my home page!

I am a doctoral student at the Media and Machines lab, where I am working with Bill Smart.

UPDATE: Yuval has uploaded the swimmer code for all to use!

I am working on developing local methods for reinforcement learning in continuous domains. I seek ways to train robots to perform complex, articulated motor tasks, without resorting to any ad-hoc dimensionality-reducing approximations or hacks. Most of my work is done in collaboration with Yuval Tassa.

Here are some of our recent demos:

Walkers

A comparison between the initial walker and a walker on stilts:

The composite controller traversing rough terrain:

A comparison between the initial gait and the trained gait for walking uphill:

Swimmers

This is a 14-dimensional system, and all the exhibited behaviors, including the optimal swimming gait, were learned.

A full interaction sequence with a gait learned through receding horizon DDP:

A comparison of the initial and the optimized gait, learned through policy gradient (PG):

A demonstration of a PG swimmer tracing a sigmoid path:

Page last modified on December 24, 2007, at 01:27 PM.