Simulation-to-Real Transfer Improves as Robotics Foundation Models Mature

## Closing the Sim-to-Real Gap A long-standing problem in robotics has been that skills learned in simulation often fail to transfer cleanly to physical hardware, due to subtle differences in friction, lighting, and sensor noise that simulators don't fully capture. ## Foundation Models Change the Calculus Robotics foundation models trained across large, varied datasets -- spanning many robot bodies and environments -- appear to learn more general physical priors that transfer more reliably than narrow, single-task simulation training did. ## What Teams Are Seeing Deployment teams report needing less real-world fine-tuning time before a simulation-trained skill performs reliably on physical hardware, shortening the gap between a working demo and a production-ready deployment.