Hands-on Physical AI with Kevin Cloutier

Kevin Cloutier is the North American Lead for Physical AI at CapGemini, a global engineering consulting firm. He is a computer engineer who is passionate about taking complex computations to the edge. He joins Chris to discuss the transition from legacy, statically programmed factory robotics to modern, imitation-learning-based “Physical AI,” building and calibrating the affordable $200 3D-printed SO101 robot arm using the LeRobot open-source framework or Intel’s Physical AI Studio, and orchestrating complex robotic systems entirely on local edge hardware. Timeline Chris welcomes Kevin Cloutier to discuss “Physical AI” and how it relates to robots and things that move in the physical world. (00:00:15) Meeting at Embedded World: They reminisce about meeting at the Canonical booth in front of a robot demonstration, which you can see in the Embedded World Demonstration Video. (00:02:10) Trade Shows and Geography: Chris and Kevin discuss Embedded World in Nuremberg, Germany, upcoming SPS, and the high concentration of manufacturing robots in Europe compared to the US. (00:03:50) The “Unicorn Developer” Shift: Kevin talks about his background in computer engineering and how the “unicorn developer” has shifted from the full-stack web developer of the 1990s to someone who can operate, program, repair, and train physical robots. (00:06:05) Factory Robots vs. Edge Cases: Contrasting legacy, statically programmed factory robots (like ABB or Universal Robots hanging car doors) with modern generative AI approaches capable of handling real-world edge cases. (00:09:15) The Robotics Software Stack: Demystifying the layers above motor drivers, including Real-Time Operating Systems (RTOS), Linux, and message-passing frameworks like ROS (Robot Operating System) and ROS 2. (00:12:40) Orchestrating Systems of Systems: Kevin describes playing tic-tac-toe using Vision Language Action (VLA) models, where a higher-level camera and computer vision system orchestrate the coordinates for the movement model. (00:15:10) The Evolution of Compute: How modern silicon, integrated GPUs, and SOCs have allowed the massive, heavy control boxes of legacy robots to shrink down to a small NUC-sized device mounted directly on the robot. (00:18:05) The SO101 Robot Arm: Introducing the SO101 3D-printed robot arm from Hugging Face, which democratizes robotics by allowing anyone to build a leader-follower setup for around $200. (00:21:20) How “Backyard Engineers” Learn: Kevin’s advice for firmware and hardware engineers stepping into robotics: follow the documentation, get it running, and then ask questions about what you don’t know. (00:23:55) Calibration and the LeRobot Framework: A look at using the LeRobot open-source framework to calibrate hobby-grade motors and define their movement limits. (00:26:40) Recording “Episodes” via Imitation Learning: How users physically guide the leader arm to control the follower arm while a webcam records the visual and servo coordinate data. (00:28:50) Training the Model: Organizing data into short 5-episode chunks to make deletion easier, and training the model locally on NVIDIA GPUs or in the cloud. (00:31:30) From Training to Evaluation: Moving from training to evaluating the custom model, and asking questions about action chunking, model stutter, and operating frequency. (00:35:10) Sensor Fusion vs. Pure Vision: The current dominance of cameras in physical AI, and the potential to fuse accelerometers and time-of-flight sensors on mobile robots like Autonomous Mobile Robots (AMRs). (00:41:00) Real-World Calibration Challenges: Kevin shares a story of someone knocking over his camera boom at Hannover Messe and how he used April Tags to quickly recalibrate the camera’s physical coordinates. (00:43:15) The Reality of Humanoids: Debunking humanoid hype and explaining why full humanoids are further out than the public thinks, due to immense hardware cost, degrees of freedom, and safety. (00:45:50) The Puppeteer behind the Curtain: Kevin points out that many impressive humanoid demos, like the Unitree G1 at Hannover Messe, are actually being teleoperated by an engineer standing nearby. (00:48:40) The Complexity of Robot Subsystems: Using Steve Crunch and the book “Exploding the Phone” as an analogy for how modern robotic systems have become too complex for a single human to fully understand. (00:52:15) Understanding SmolVLA and SmolVLM: Diving under the hood of Vision Language Action models, which are fine-tuned transformer models that translate visual inputs into physical robotic coordinates. (00:55:10) Local AI and the NPU: How modern System-on-Chips (SOCs) let engineers run models locally on the CPU, GPU, or Neural Processing Unit (NPU) using optimization toolkits like Intel OpenVINO. (00:58:15) The Joy of the Physical World: Why working with physical hardware and robots is far more creative and rewarding than optimizing spreadsheets or SaaS applications. (01:04:30) “If the Robot Can’t Kill You, It’s Not Fun”: Kevin shares his colleague’s favorite metric for a truly exciting robotics project. (01:07:10) Advice on ROS and “Cobbling”: Starting with duct tape and bubble gum, and learning message-passing frameworks like ROS only when you need to coordinate multiple independent robots. (01:09:30) Physical AI Studio Demo: An invitation to see Kevin showcase Intel’s Physical AI Studio with multiple active robots at the upcoming AI Infra conference in Santa Clara. (01:14:45) Find Kevin online at his website cloutier.engineer or on LinkedIn. (01:20:10)