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.

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

As a benchmark, FolDeX exposes these unresolved challenges and provides a physical testbed for future methods addressing negative transfer, catastrophic forgetting, and cross-embodiment action alignment.

— FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects  (2609.10243 - Liu et al., 9 Sep 2026) in Section 4.4, “Preliminary Observations on Other Transfer Axes” (the subsection labeled “Preliminary Observations on Other Transfer Axes”)