Robotics Needs Its GPT-3 Moment

🔥 What happened Perry Dong and Chelsea Finn from Stanford argue that while robot pretraining produces impressive demos, reliability for autonomous deployment is missing. They call for a universal post-training recipe analogous to LLMs. 💡 Why it matters A robot that loads dishes correctly 95% of the time will break something weekly in a home with glass and kids. Unlike LLMs, there's no human review before action—a bad robot move just happens. RL for robotics is currently a craft, not a recipe. ⚡ Our take Anyone who thinks scaling alone solves robotics ignores the hard truth: without a standardized post-training pipeline, humanoid household robots remain demo objects.