Address scarcity of high-quality robot manipulation data for training generalist policies

Develop scalable data collection, curation, or learning approaches that alleviate the scarcity of high-quality robot manipulation demonstrations needed to train generalist robotic manipulation policies effectively.

Background

Training generalist policies benefits from large and diverse datasets, yet collecting high-quality robot demonstrations is slow and expensive. The authors combine internal and public data but still note scarcity of high-quality data as an open issue.

Solutions that reduce dependence on costly data or improve data quality at scale would strengthen the reliability and generalization of LBMs.