The sim-to-real transfer gap is assumed to grow with task complexity, but we find a U-shaped relationship. Across 6 manipulation tasks (reaching, pushing, pick-and-place, stacking, insertion, bimanual assembly) with 5 domain randomization levels on Franka Emika: simple tasks transfer well (gap 8-12%), moderate tasks show maximum gap (28-41%), complex tasks show reduced gap (18-24%).
Transfer learning with foundation models like Geneformer has shown promise for cross-disease prediction in neurodegeneration, but methodological concerns about cell-type composition confounds remain unaddressed. We conducted cell-type stratified experiments across Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), fine-tuning Geneformer within four homogeneous cell populations.
Neurodegenerative diseases share core transcriptomic programs — neuroinflammation, mitochondrial dysfunction, and proteostasis collapse — yet computational models are typically trained in disease-specific silos. We investigate whether a single-cell RNA-seq foundation model fine-tuned on one neurodegenerative disease can transfer learned representations to others.