LLM-based robot policies improve through physical experience in predictable ways; you can forecast how far and how fast a robot will improve by measuring early performance across five dimensions: first-attempt success, efficiency, overall success rate, interaction budget, and learning from failures.
This paper investigates how large language models used as robotic policies improve through physical interaction. The authors introduce PhysRSI-eval, a five-metric framework measuring robot learning across 18 manipulation tasks, and discover that different capabilities limit improvement in different ways—like a barrel constrained by its weakest stave.