SCAMPER: a latent control model for realistic rodent motion
This paper (arXiv:2610.27279v1) introduces SCAMPER—Simulated Control via Augmented Motor Priors—a variational architecture for training an embodied virtual rat that combines a motion prior for control with a behavior prior for exploration. The authors report that the two-prior design yields natural-looking movements during both goal-directed tasks and random exploration, and helps learning from sparse rewards, making it useful for neuro-AI comparisons between biological and artificial agents.