Q&A: How video helps build robot brains for physical AI

Robotics companies are building industrial and humanoid robots, using AI to help them perceive and act in real environments. Many approaches rely on feeding robot systems YouTube videos to orient devices visually and spur physical AI. Anaxi Labs takes a different approach by crowdsourcing videos of people performing tasks and sharing that data with robotics makers. Human-scale video is used to capture how robots should perform tasks under real circumstances. This method aims to create a clearer roadmap for physical AI by building robot-specific data infrastructure, since robot training data cannot be sourced from the internet like large language model data.

"Robots could well be the next trillion-dollar tech opportunity, in no small part thanks to AI. Not surprisingly, that's led to race by a variety of robotics companies to build industrial and humanoid robots to help (or replace) humans. And to help orient those devices visually in the real world, robot brains are being fed Youtube videos. The idea is to help them understand the environment in which they would work and to spur physical AI."

"Kate Shen, co-founder of startup Anaxi Labs, is following a different approach to training robot brains. She is crowdsourcing and supplying videos of people performing tasks, which she then shares with robotics makers. Human-scale video, she argues, is critical to train robots because it more accurately captures how robots should perform their tasks, depending on the circumstances around them. More broadly, the technique can also provide a clearer roadmap for physical AI."

"This is very much a ... [Carnegie Mellon University] startup. We started this company [when] we realized that when it comes to AI-building [large language models] (LLMs), everybody knows that there are two things on the infra level, chips and data. The same things were happening to robotics as we moved from digital to physical AI."

"Except this time..., everybody's aware of [the] difficulty, everybody's using infrastructure. But when it comes to data, we have to build the data infrastructure from scratch, because unlike LLM, the training data for robots can't be from the internet. We realized that it would become a [barrier] sooner or later, and it will turn into a major, major indu"

Read at Computerworld