Robot Learning with Super-Linear Scaling

Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose *Crowdsourcing and Amortizing Human Effort for Real-to-Sim-to-Real* (**CASHER**), a pipeline for scaling up data collection and learning in simulation where the performance scales superlinearly with human effort. The key idea is to crowdsource digital twins of real-world scenes using 3D reconstruction and collect large-scale data in simulation, rather than the real-world. Data collection in simulation is initially driven by RL, bootstrapped with human demonstrations. As the training of a generalist policy progresses across environments, its generalization capabilities can be used to replace human effort with model-generated demonstrations. This results in a pipeline where behavioral data is collected in simulation with continually reducing human effort. We show that **CASHER** demonstrates zero-shot and few-shot scaling laws on three real-world tasks across diverse scenarios. We show that **CASHER** enables fine-tuning of pre-trained policies to a target scenario using a video scan without any additional human effort.