we gave @colossusmag a look at what @mvernal @pranavreddy @igarciacamargo me & team @conviction are trying to do — find and arm the most extraordinary founders at a moment when technology molds a malleable future feels like a mission! but yes, acting with belief is also a wager

@tonyzzhao we forgot to connect Memo to the wifi! Oh wait we don’t need it. We moved our robot policy to run completely on-device. Why? Because “works great until the WiFi hiccups” is not an acceptable failure mode for a robot operating in your home. Local inference: predictable latency, no surprises.

@ArpitKalla not @arpit 😭 I think the 3hr video caused some kind of bug in @X After first drafting the thread I made bunch of edits but they didn't get reflected in the actual post. I also wasn't able to edit the post 10min after the initial post.

In January, I started "building something new" with an incredible team. Today I finally get to share some first details about what we've been building. We've called it Walden Robotics ( I thought long and hard about my own reasons for starting this company. It's not only about the robots. It's also about people. I've tried to capture those thoughts in my first Walden blog post: It's been an incredible ride so far. Within just a few months of forming the company, we were already operating a general-purpose robot with an end-to-end policy in production in one of the most important factories in North America. It's amazing at how much I've already learned from that experience. There is a lot of work to do, but the mission has never been so clear. Please help me welcome Walden Robotics into the world. And stay tuned for more updates!

Personal update: I've joined @sundayrobotics. Two questions ran through my whole PhD: how to learn from scalable human data, and how to build general-purpose robots. Trying to answer them convinced me of one thing: general-purpose robots will never come from better models alone. It takes tight iteration across data, hardware, model, control, and evaluation. Every loop you can shorten matters. My first dinner with @tonyzzhao and @chichengcc turned into a four-hour conversation. I walked away realizing how much we saw eye to eye: scale the data, think full-stack, start from the problem you want to solve instead of the idea you want to win. So getting to work at Sunday is a dream come true, a place to solve generalization with the full breadth of human data and system-level thinking, and keep chasing the questions I care most about. After my first month in, two things stand out: Sunday’s full-stack team iterates unbelievably fast, and the energy when everyone is aligned on the same vision is electric. This speed and energy is exactly why what used to feel impossible now feels close. Home robots, the frontier physical AI in the hands of ordinary people, were long seen as a distant dream . At Sunday, I watch this dream take shape every day. I'm convinced there's real research-market fit here: foundation models and home robots point toward the same north star, generalization, not specialization, because every home is different. Excited for the zero-to-one moment ahead.

What's different between these two BC policies? It's the same architecture, training budget, and data collection setup — the only difference is the controller gains! Controller gains are an understudied design parameter in robot learning. In our new work (w/ @BronarsToni*, @pulkitology), we show how they act as an inductive bias across BC, RL, and Sim2Real transfer, with real consequences on performance. Here's what we found 🧵 * Equal Contribution 📄arxiv: 🔗website: