RobTalk. The autonomous robotics podcast from RobCo.
Real talks on Physical AI. What works. What breaks.
From first deployments to systems that handle real-world complexity.
Insights for engineers, operations leaders, and robotics enthusiasts.
New episodes every month. Subscribe on Spotify, Apple Podcasts, or wherever you listen.
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Données mises à jour le 12/10/2026
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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
How Robots Learned to Walk: From Hand-Engineered Control to Reinforcement Learning
jeudi 27 août 2026 • Durée 42:46
Is legged locomotion actually a solved problem?
In this episode of RobTalk, Felix Frank from our Robot Intelligence team explains how legged robots learn to walk, and why going from an impressive stage demo to a reliable real-world deployment is still one
of the hardest open problems in robotics.
You'll gain insights into:
- Why footstep planning used to mean months of hand-engineered optimization
- How GPU-parallelized simulation and domain randomization changed the entire approach
- What retargeting means, and why human motion data now trains robot policies
- The difference between imitation learning and adversarial motion priors
- Why legged robots face real safety and power challenges that fixed robots don't
- What is still unsolved: combining blind whole-body control with real terrain understanding
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
01:14 – Rob Talk intro & welcoming Felix Frank
01:49 – Felix's background
02:38 – Breakout projects at VW (e.g., compressed air control)
03:45 – Move into humanoid robotics (US startup, whole-body control)
04:23 – The classical engineering approach: footstep planning & online optimization
06:33 – Sensor fusion: IMUs, contact sensors & Kalman filtering
08:39 – What is a kinematic tree?
10:08 – Limits of the classical approach (door opening, manipulation)
12:19 – The optimization problem: cost functions & constraints
14:40 – Boston Dynamics' Atlas & the limits of hand-engineering
17:18 – The paradigm shift: GPU-parallel simulation & the Unitree G1
18:11 – Reinforcement learning explained: reward functions & domain randomization
23:50 – Domain randomization in depth
25:26 – Building robustness through external perturbations in training
26:23 – Motion imitation: mocap, retargeting & DeepMimic (2018)
30:57 – The data-centric approach: large-scale datasets & NVIDIA Sonic
33:39 – Why the humanoid form makes sense (locomotion vs. manipulation)
34:54 – Blind locomotion: how far can you get without perception?
36:35 – Terrain awareness & planner components
39:20 – Legged vs. wheeled robots: safety & fail-safe behavior
Scripted Demos vs. Real Autonomy: The Truth About Dancing Robots
jeudi 30 juillet 2026 • Durée 30:37
Do dancing robots we see prove that autonomous robotics has been solved?
Autonomous Robotics are becoming more impressive every year. They can dance, run marathons and perform movements that seemed impossible just a few years ago.
But does that mean they’re ready for real industrial applications?
This episode takes an honest look at where the technology actually stands, what those demos can and cannot do, and what the next real milestone looks like.
You’ll gain insights into:
- Viral robot videos being scripted,, and what that means
- Robots having gone from not being able to stand 10 years ago to running marathons today
- Movement vs. understanding: the gap nobody talks about
- What would actually impress an engineer instead of seeing a backflip
- How close we are to robots working in factories for eight hours straight
More about RobCo:
Website:https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Dancing robots: milestone or marketing?
03:15 Why did it take 30 years to get here?
07:02 What finally changed the game
15:49 Can robots do more than just move?
19:00 Where does movement end and intelligence begin?
24:07 What is actually holding robots back?
26:17 How far away is real factory deployment?
Industry 5.0: Future of Manufacturing?
jeudi 25 juin 2026 • Durée 44:01
Industry 5.0 is not a tech upgrade. It is a different question entirely.
Where Industry 4.0 asked what machines can do, Industry 5.0 asks where we want to be as a society. That shift changes everything: Who writes the standards, what factories are optimized for, and what the real role of AI and robotics actually is.
You'll gain insights into:
- what Industry 5.0 really is, why it comes from a completely different place than 4.0, and what that means for how technology is built and used
- the three pillars that define it: human centricity, sustainability and resilience
- how RobCo is already putting these principles into practice
More about RobCo:
Website:https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Industry 5.0: Rebranding or real shift?
02:26 Industry 1.0
08:07 Industry 2.0
15:09 Industry 3.0
18:20 Industry 4.0
25:49 Industry 5.0
33:40 Why robots are the only path forward
35:16 RobCo's role in Industry 5.0
41:51 What's happening next?
How Robots Turn Language into Motion: The AI Stack Behind Physical AI
jeudi 28 mai 2026 • Durée 43:31
How do robots go from human instruction to real movement?
Telling a robot to “pick up a box” sounds simple.
But behind that command is a complex chain of decisions: understanding language, interpreting the environment, choosing the right action and turning it into physical movement.
In this episode, Clemens (Principal Engineer) and Robert (Robotics Engineer & Researcher) explain how RobCo approaches this challenge with ALFIE - combining classical robotics, AI models, sensors, safety systems and real-world industrial requirements.
You'll gain insights into:
- the three-layer hierarchy (System 2 / System 1 / System 0) that turns language into motor currents
- why physical grounding is the hardest unsolved problem in robotics today
- how 100-200 demonstrations are enough to fine-tune Alfie on a new use case
- why methods that brought man to the moon are now central to physical AI
More about RobCo:
Website:https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Controlling robots with language
00:32 Meet Clemens and Robert
02:22 System 2, 1, 0: How robots think
04:35 The driving analogy explained
06:28 What's the hardest part of the chain?
07:15 Translating language into robot action
08:43 What really happens when you say "pick up the glass"
11:04 Why neural nets find their own language
15:21 Introducing Alfie
21:09 Pre-training + fine-tuning a robot
24:49 How commands become motor currents
28:31 Top 3 questions from Hannover Messe
35:04 The funniest moment at the trade fair
38:02 What makes Alfie different
40:28 World models: The next big unlock?
How to Teach a Robot: From Moving Arms to Autonomy with Physical AI
jeudi 30 avril 2026 • Durée 41:14
How do you actually teach an AI-powered robot?
For decades, robots in industry have followed one principle: You program every single step.
Every movement.
Every position.
Every exception.
And if something changes, you start again.
That approach is reaching its limits.
As environments become less structured and processes more dynamic, the question shifts:
How do you move from programming robots… to teaching them?
You'll gain insights into:
- how to physically guide a robot arm
- what a VR headset, a gripper replica, and a helmet camera have in common
- why data quality matters more than data quantity
- how close we really are to just talking to a robot and getting an answer
More about RobCo:
Website:https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 How do you actually teach an AI robot?
01:13 Traditional robot programming
03:08 RobFlow: no-code meets the factory floor
05:30 Overview: Five ways to teach a robot
06:21 Method 1: moving the arm by hand
08:23 Method 2: the leader arm and haptic feedback
10:41 Method 3: VR goggles as a teaching device
15:39 Method 4: the gripper replica in your hand
17:47 Method 5: motion capture and ego data
22:00 Rich data vs. massive data: What works better?
27:09 How far away is voice-controlled robotics?
31:10 Why humanoid hardware is still the bottleneck
35:42 Learning robots open a completely new dimension
39:00 We're using AI like a typewriter, what's next?
90% of Robot Demos Never Make It to a Real Factory. Here's Why.
mardi 31 mars 2026 • Durée 25:53
90% of robot prototypes never make it to real factories.
They work in a closed lab. They look impressive on video. And then reality hits.
In this episode, we break down what actually separates a convincing prototype from a system that runs reliably in production. And why that gap is much harder to close than most people think.
You'll gain insights into:
- what makes a prototype fail in real deployment
- why 99% reliability is harder than it sounds
- how the digital twin works inside a neural net
- where humanoid robots really stand today
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Intro
02:00 Why robot prototypes are often misleading
05:41 Reliability beats impressive capabilities
07:46 Why RobCo builds end-to-end solutions
09:27 48-hour testing & real-world data loops
12:23 How closed learning loops actually work
14:09 Digital twins explained simply
17:49 The digital twin as a map of the real world
19:57 How physical AI filters relevant information
22:32 What people will misunderstand about physical AI
24:51 Humanoid robots: hype vs. reality
Physical AI: The 5 Levels of Robot Autonomy explained
mardi 31 mars 2026 • Durée 31:09
Dancing robots. Kung-fu moves. Humanoid acrobatics all over the feed.
But does that mean Physical AI has actually arrived?
In this episode, Clemens, Principal Engineer at RobCo, shares what Physical AI really means for industrial automation and where the technology stands today.
You'll gain insights into:
- why Large Language Models are just the starting point and what comes after
- how robots are being taught today compared to five years ago
- how RobCo approaches Physical AI in real manufacturing environments
- where the technology stands today and what accuracy rates actually matter in practice
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Intro
00:48 Where physical AI stands right now
01:41 From chatting to grabbing: the next AI leap
02:43 Why physical data is so hard to collect
04:35 What physical AI actually means at RobCo
06:16 Why 100 years of automation hit a wall
08:18 The five levels of robot autonomy
13:27 Hardware, software, data
15:15 Why end-to-end ownership changes everything
16:19 Teaching a robot in a few hundred moves
18:09 Why software turns a robot into a brain
19:07 Why modular beats fixed automation
22:04 Real use cases already running in factories
24:26 How many nines does a production line need?
28:20 The moment factories realize everything changed
Why robotics is changing everything right now | Clemens Marschner
mardi 31 mars 2026 • Durée 46:28
How do you go from being a computer passionate to helping shape an AI-first robotics company?
This episode is about Clemens.
His journey.
His decisions.
And why he is exactly where he is today.
You’ll gain insights into:
- his path from early fascination with computers to working in large-scale tech environments
- why he chose to join RobCo
- what truly fascinates him about AI (beyond the buzzwords)
- how engineering changes when systems start learning instead of just executing rules
- how teams at RobCo collaborate, make decisions, and drive innovation forward
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Welcome to the RobCo Podcast
00:35 Clemens' Role as Principal Engineer
01:20 A Computer Kid Since Day One
03:07 PhD, Linguistics & Early Machine Learning
09:14 Microsoft, Bing & Web Search Ranking
11:44 Autonomous Driving: Hype vs. Reality
16:02 Ride-Sharing & Building Lyft's Maps
19:49 Why Clemens Never Left Munich
22:33 How RobCo Clicked Immediately
24:27 Speed, Culture & Everything Under One Roof
30:09 Leading the Autonomy Team
33:26 Rapid Fire Questions
44:41 Final Words & Why RobCo Is Hiring
RobTalk - Trailer
vendredi 27 mars 2026 • Durée 00:32
RobTalk. The autonomous robotics podcast from RobCo.
Real talks on Physical AI. What works. What breaks.
From first deployments to systems that handle real-world complexity.
Insights for engineers, operations leaders, and robotics enthusiasts.
New episodes every month. Subscribe on Spotify, Apple Podcasts, or wherever you listen.
