John Schulman

Welcome to my stodgy academic homepage!

I'm a research scientist at OpenAI. I received my PhD in Computer Science from UC Berkeley, where I had the good fortune of being advised by Pieter Abbeel. My primary interest is reinforcement learning, and I believe that motor learning is key to many aspects of intelligence. My work on policy optimization made it possible for a robot to learn to run and get up off the ground (in simulation). I am co-teaching a course on deep reinforcement learning this spring (2017) at UC Berkeley.

My current research is inspired by my earlier work in robotics, where I mainly investigated the following two problems: (1) teaching robots to perform manipulation tasks using human demonstrations, work that enabled autonomous knot tying and surgical suturing; (2) using trajectory optimization for motion planning. The software library developed for this project has been used on a variety of real robots, including one scary humanoid.







Sometimes people ask for slides or videos of my presentations, so I'll keep some recent links here.

Theme from here and here.