RoboFL: Federated Expert Assembly for World Action Models
Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heterogeneous. A natural federated solution is to let each client adapt a shared foundation model through parameter-efficient fine-tuning, avoiding the exchange of full-model updates. However, federating these adapters is nontrivial, as naive aggregation can entangle incompatible updates, while incorporating MoE-style routing into federated aggregation may dilute specialization and destabilize expert selection. We present RoboFL, which instantiates MoSAIC (Mixture of Slotted Adapters) for federated world-action learn...
| Authors | Rongyu Zhang, Ruizhi Fan, Yunfan Lou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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