How Robots Turn Language into Motion: The AI Stack Behind Physical AI

How Robots Turn Language into Motion: The AI Stack Behind Physical AI

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 [https://www.rob.co] LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/ [https://www.linkedin.com/company/robco-therobotcompany/] Instagram: https://www.instagram.com/robco_therobotcompany/ [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

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 [https://www.rob.co] LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/ [https://www.linkedin.com/company/robco-therobotcompany/] Instagram: https://www.instagram.com/robco_therobotcompany/ [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.

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 [https://www.rob.co] LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/ [https://www.linkedin.com/company/robco-therobotcompany/] Instagram: https://www.instagram.com/robco_therobotcompany/ [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

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 [https://www.rob.co] LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/ [https://www.linkedin.com/company/robco-therobotcompany/] Instagram: https://www.instagram.com/robco_therobotcompany/ [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

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 [https://www.rob.co] LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/ [https://www.linkedin.com/company/robco-therobotcompany/] Instagram: https://www.instagram.com/robco_therobotcompany/ [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