When one observation admits several valid actions, CVAE KL regularization and flow-model Lipschitz smoothness decide whether a policy keeps multimodality, while standard robot simulation benchmarks turn out to be nearly unimodal
The work formalizes multimodality in behavioral cloning, proves that latent-variable policies preserve demonstrated modes only if the latent carries action-conditioned information and that excessive posterior-prior regularization suppresses it, shows that action-space generative policies are limited by the Lipschitz constant of the base-to-action map, validates these mechanisms on synthetic multimodal navigation and a real-robot bimodal tissue-grasping task, and uses a GMM modal-clustering diagnostic to find limited conditional multimodality in Push-T, UR3, LIBERO, and Meta-World, where deterministic regression remains competitive.