I’ve been joking with friends that my main job this year has been interviewing 🫠 Now I’m finally excited to share what’s next: I’ll be joining NYU as an Assistant Professor next fall, and before that, I’ll be spending a year at @physical_int ! I feel incredibly fortunate to have spent the past two years at UCSD working on control-hardware co-design. When I was looking for a postdoc, I told people I wanted to work on robot learning for hardware design. I’m deeply grateful that @xiaolonw and Mike trusted me enough to start something none of us quite knew how to do. Whenever I mentioned working with both of them, people either knew only one of them (they come from very different communities), or found the combination surprising. I find it to be really fun and I learnt so much more than I would anywhere else. Although I think jointly optimizing policies and hardware at scale is still somewhat early, I strongly believe in optimizing (or at least iterating) hardware alongside policy training. Over the past few decades, we’ve developed a lot of depth in individual hardware components: sensors, actuators, mechanisms, materials, etc. But we still haven’t explored enough of their combinations as integrated systems, or understood their actual usefulness for policy. As coding agents get more and more powerful, learning where and how hardware is bottlenecking robots is really valuable. And the only way to understand this is to scale up and try things :) I’m getting ready to join the bay area robotics community, but it’s still a bit sad to leave the beautiful San Diego. I had a lot of fun sampling taco shops on Tuesdays (although I eventually converged to two places on my way home...). I will definitely miss the on campus climbing gym, and the forever-sunny-and-warm outdoor swimming pool next to it 🥺