RobTalk

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

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 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 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 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?