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.

References

Despite progress in training generalist policies, challenges such as catastrophic forgetting, data heterogeneity, scarcity of high-quality data, multimodal fusion, handling dexterity, and maintaining real-time inference speed remain open research problems.

— A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation (2507.05331 - Team et al., 7 Jul 2025) in Section 2.1, Related Work—Robot Learning at Scale