Gaussian Distillation of Flow Policies

Flow policies are among the strongest robot controllers and are well suited to modelling multimodal action distributions. For robot manipulation tasks, we show empirically that this capability is not always needed and propose the Gaussian Action Readout (GAR), a Gaussian model trained with 0.02% of eligible states on frozen readout features. It samples with one action-network pass, gives closed-form likelihoods, and achieves near-teacher task success at up to twice the inference speed.