How to Train Your Robot
Summary
The article surveys how robotics teams collect training data for AI-powered robots, comparing teleoperation, reinforcement learning, and simulation-based approaches. It explains domain randomization and ADR to bridge sim-to-real gaps, discusses egocentric video and UMI data collection, and highlights the economics and scale challenges of acquiring massive robot data, concluding that real-world data will drive future progress but remains costly and variable.