FAME combines a factor-aware MoE with frozen pretrained encoders via staged adapter training and joint fine-tuning, reporting 34% gains on Meta-World and 35% in real-world pick-and-place under environmental changes.
Diffusion policy: Visuomotor policy learning via action diffusion,
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Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
FAME combines a factor-aware MoE with frozen pretrained encoders via staged adapter training and joint fine-tuning, reporting 34% gains on Meta-World and 35% in real-world pick-and-place under environmental changes.