Why Robots Still Struggle With Simple Tasks (And What Might Finally Change That) | Karol Hausman, Co-Founder & CEO of Physical Intelligence

Physical intelligence represents the next frontier in robotics, shifting from rigid, pre-programmed machines to generalist models capable of navigating diverse environments. By integrating large language models with robotic motion, these systems leverage vast internet-based knowledge to generalize across tasks, moving beyond the limitations of traditional, task-specific programming. Real-world data collection remains critical for manipulation, as simulation often fails to capture the complexity of physical interactions. Reinforcement learning further enhances reliability by optimizing for task success rather than mere imitation, allowing robots to compensate for hardware imperfections. Karol Hausman, CEO of Physical Intelligence, emphasizes that true progress requires a long-term research focus, prioritizing the development of a foundational "AI brain" over short-term commercial applications. This approach mirrors the evolution of language models, where scaling generalist architectures consistently outperforms specialized, narrow solutions.