 All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free observations to RL policy. VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
<clarity>  All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free observations to RL policy. VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
 All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free visual input to the RL policy learner. Our Visual Attention and Invariance (VAI) method significantly outperforms the state-of-the-art on visual domain generalization, gaining 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode .