Roboticists historically traded science-fiction ambitions for specialized industrial arms. This gap between complex goals and simple outputs defined the field for decades. MIT Technology Review examines how modern learning methods bridge this divide. Practitioners now shift from rigid programming toward flexible, data-driven behaviors to achieve general-purpose utility in physical spaces.