Summary: We discuss big-picture aspects of robotic deep learning, such as why robotic imitation learning makes sense from a product standpoint.
Host: Kevin Zakka
Guests: Pete Florence and myself
Summary: We discuss progress in robotics in the last 50 years, and the roles software and hardware have played in this development. On the algorithmic side, we discuss how much of imitation learning and reinforcement learning is needed to obtain general purpose robots and why policy evaluation is hard in the real-world.
Summary: We cover how I got into AI research (starting from neuroscience!) and the hard-won lessons I learned from my first few projects at Google. This is a summary of what I have learned about robotic deep learning in the last 5-6 years. Hopefully folks who are curious about what a “5-year growth trajectory, starting from 0 experience” find this useful.