World Models & the next step for Physical AI
Episode von RobTalk von RobCo · 24. September 2026 · 46 min
Is 2026 the year world models make Physical AI real?
Vision-Language-Action models have become the standard approach to robot learning over the last few years. In this episode, Felix Frank from our Robot Intelligence team explains why the next shift is already underway: world models that predict what happens next before a robot decides what to do.
We cover how the field moved from small, task-specific models to large pretrained architectures, why memory and context work differently in robotics than in language models, and why robots still need to run at high frequency with very limited context per decision.
We also break down the two competing approaches to building a world model: one that generates full video in pixel space, and one that predicts directly in a compressed latent space, closer to the JEPA approach.
Finally, we talk about where world models actually help today: generating synthetic training data, predicting the action needed to reach a desired future state, and simulating multiple possible outcomes before choosing one to execute.
You'll gain insights into:
- Why Vision-Language-Action models became the default approach in robotics
- How robots handle memory and context differently than language models
- Why control frequency, not context length, is the real constraint in robotics
- Two different ways to build a world model, and the trade-offs between them
- Why touch and force sensing matter as much as vision for some tasks
- Why mechanical and electrical engineering remain the next big bottleneck
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
#physicalAI #robco #robotics #autonomy #podcast #worldmodels
01:38 The 2026 state of Physical AI
02:50 From task-specific models to the ChatGPT moment
07:09 The data bootstrapping problem in robotics
09:21 What goes into a Vision-Language-Action model
11:15 Why "state" matters, and the Markov assumption
17:10 Context length: robotics vs. language models
18:43 Inside the control hierarchy: motors to orchestration
20:05 Onboard GPUs vs. cloud compute
20:44 The human latency baseline
21:11 What is a world model, really?
22:34 Video generation as a world model
25:30 Is a language model already a world model?
27:02 Two ways to build a world model
30:48 Three ways world models help robots today
34:08 What happens in the next six to twelve months
35:35 Handling multiple possible futures
37:52 Beyond vision: touch and force sensing
39:19 The real bottleneck: mechanical engineering
40:46 How fast the field moves, and RobCo's 24/7 goal
42:36 Inside RobCo's experimentation framework