Robot Learning in Industry: How Hardware Innovation Is Accelerating Real-World Robot Training скачать в хорошем качестве

Robot Learning in Industry: How Hardware Innovation Is Accelerating Real-World Robot Training

Welcome to Robot Learning in Industry, a Neuracore original series exploring how machine learning is transforming real-world robotics. Each episode breaks down the practical challenges, emerging tools, and engineering breakthroughs shaping how ML is deployed outside the lab in factories, warehouses, hospitals, and beyond. In this episode, Neuracore’s we sit down with the team at Trust Robotics, the hardware innovators behind the manipulators used in Stanford’s viral ALOHA project, a moment that helped accelerate the entire robot learning field. Their conversation offers an inside look at what it really takes to apply machine learning to physical systems at scale. 𝗧𝗵𝗲𝘆 𝗿𝗲𝘃𝗲𝗮𝗹: • The hidden hardware bottlenecks slowing down ML-driven robotics • Why many teams are still gathering data incorrectly and how it harms policy performance • The growing importance of torque transparency and next-generation actuator designs • What is required to make long-horizon, real-world robot policies work reliably • The integrated hardware and software stacks that will shape the next phase of physical AI Featuring unfiltered insights from engineers with years of experience building robots that operate outside controlled research settings, this discussion cuts through the hype to show what is truly driving progress and what is holding it back. Chapters: 00:39 - Who Is Trossen Robotics? 03:17 - Biggest technical challenges. 13:14 - Transition from hardware to software solutions. 18:22 - Keeping up with evolving ML models 24:14 - Hardware challenges 34:10 - Edge versus cloud computing 41:22 - What's next for Trossen Robotics? Learn more about Neuracore: https://www.neuracore.app/ Learn more about Trust Robotics: https://www.trossenrobotics.com/ Subscribe to Robot Learning in Industry for more conversations with the teams bringing machine learning into the real world. #robotics #ai #machinelearning #robotlearning #automation #hardware #Neuracore #TrustRobotics #physicalAI