Can robots learn from the internet the same way ChatGPT learned from text?

In this episode, Andrew Wooten, co-founder of Rhoda AI, explains why his company believes the future of robotics isn’t collecting millions of hours of robot data ... it’s learning from internet-scale video. Instead of relying on traditional vision-language-action (VLA) models that require enormous training datasets, Rhoda’s approach teaches robots physical intuition by predicting the future through video.

We also explore why, in Andrew's opinion, warehouses and factories will likely be the first major market for humanoid robots (not homes!), why Rhoda chose a wheel-based humanoid design, how language models fit into physical AI, and how the company’s robots can learn complex tasks with just 8–10 hours of training data instead of 10,000+ hours.

If you’re interested in robotics, AI, automation, or the future of manufacturing, this conversation offers a fascinating look at where physical AI is heading.

In this episode:

* Why warehouses beat homes as the first market for humanoid robots

* Why Rhoda chose wheels instead of legs

* The biggest limitation of today’s robot AI models

* How internet-scale video teaches robots physics

* Why predicting the future helps robots manipulate the real world

* Edge AI vs. cloud robotics

* The role of LLMs in controlling robots

* How Rhoda cut robot training from 10,000+ hours to just 8–10 hours

* When zero-shot robot learning could become reality

Guest

Andrew Wooten

Co-founder, Rhoda AI

Website: https://rhoda.ai

00:00 Why Humanoid Robots Don’t Have Wheels

00:18 Can Robots Learn From Internet Video?

00:42 Best Use Cases for Humanoid Robots

02:10 Why Warehouses and Factories Come First

04:02 The Economic Impact of Robotics

05:00 Home Robots vs. Industrial Robots

06:05 Why Rhoda AI Chose Wheels

08:00 Building a General-Purpose Robot

09:55 Why Full-Stack Robotics Companies Have an Advantage

10:40 The Evolution of Physical AI

12:20 Why Vision-Language-Action Models Fall Short

14:05 Training Robots With Internet-Scale Video

16:05 How Rhoda AI’s Video-Action Model Works

17:25 Edge AI vs. Cloud Computing

19:05 How Robots Develop Physical Intuition

20:40 Predicting the Near Future in Real Time

22:00 Can Robots Build a Subconscious?

23:10 Using Language Models to Control Robots

25:15 Rhoda AI’s Hardware Strategy

26:20 The Biggest Problems With Today’s Humanoids

28:20 When Will Robots Truly Learn on the Job?

30:05 Training Complex Tasks in 8–10 Hours

31:15 Zero-Shot Robot Learning and What Comes Next