One Brain, Any Robot: Skild AI's Skild Brain Explained - Ep. 295

Robotics is a data problem. Like, unlike language or vision, there is not much data in robotics. There is no Internet of robot data. So if that's the scenario, we cannot pick and choose which data we use. So we go in a most general fashion every single instance of our brain which we deploy for any kind of task or any form factor that contributes in making the brain better for the future scenarios. Welcome to the NVIDIA AI podcast. I'm Noah Kravitz. I'm here today with Deepak Pathak and Avanav Gupta from SKILLD. SKILLD is a robotics company that's building the Omni Brain, a universal brain that can power robots across any form factor to tackle any task. It's amazing stuff. Very excited to find out about it from the source. And so let's get into it. Deepak, Avanav, welcome. Thank you so much for joining the AI podcast. Thank you so much for having us. So Deepak, maybe you can start and tell us a little bit about the company, about SKILLED, and then you can both talk a little bit about your roles. Yeah. So at SKILLED, as you mentioned, we are building a general purpose brain. So we call this only bodied intelligence. Any robot, any task, one brain. So think of like what GPT is for language. We are building a general brain for any physical device or any kind of robot. So this is absolutely general. Like, you can have a humanoid or a dog like robot or a robotic arm on a conveyor belt all being controlled by the same shared brain, shared intelligence behind the scene. So why do we go so general? And the reason is robotics is a data problem. Like unlike language or vision, there is not much data in robotics. There is no Internet of robot data. So if that's the scenario, we cannot pick and choose which data we use. So we go in a most general fashion every single instance of our brain which we deploy for any kind of task or any form factor that contributes in making the brain better for the future scenarios. So this is the main, goal, behind this. Yeah. And personally in my role, I have been we both have been professors before this so we are extremely technical. We have been involved in in bringing of these technologies, in the robot learning area for the last decade and more. So our role is both on the technical side to make sure that these things get built and they are super general, transferable. But our focus is also a lot on deployments. Right. Like we do not believe deployment to be a it's not hindsight, a scenario. Like for for for instance, in case of Charge GPD or or language models, folks did research for several years, but once it was ready, you have million users in seven, seven days. Only one day. I don't remember. Maybe 100,000,000 users …