Handle data heterogeneity when training generalist robotic manipulation policies

Develop methods that robustly accommodate heterogeneous robot demonstration data—spanning diverse sources, tasks, and collection conditions—when training generalist robotic manipulation policies, without degrading performance or stability.

Background

The authors aggregate large datasets from multiple sources to pretrain LBMs and observe the importance of scale and diversity. However, they explicitly identify data heterogeneity as an open challenge for generalist policies.

Robust solutions are needed to reconcile dataset inconsistencies so that scaling data continues to yield reliable performance improvements.