Teaching Robots at Scale: Data, Programming, and the Real Limits of Industrial AI (EN)

Despite decades of investment in automation, many high-value industrial operations in manufacturing remain largely manual. The limiting factor is no longer hardware – lack of machines or robots – but software. Programming automation of complex tasks is slow, expensive, and dependent on expert and domain specific knowledge.

While AI has been transforming software systems towards higher autonomy, making production environments autonomous is fundamentally harder. Conversational AI can learn through access to massive datasets on the internet, however, there is no such treasure trove or robot data.

This talk will analyze why today’s AI successes in robotics focus on controlled, low-value tasks, and why expectations around humanoids and general-purpose robots remain misaligned with industrial reality. The core challenge is data generation: without systematic ways to capture manufacturing and production data, learning-based robotics cannot scale. And without measures to protect the data and control the learning process, the solutions cannot be trustworthy, nor safe. Our approach to reliable data foundation required for the adoption of AI in robotics will be shared with the audience to open discussions about its benefits, challenges, and opportunities.