VLAIntermediate
MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models
This preprint introduces MixVLA, a model-agnostic training strategy for vision-language-action (VLA) policies that separates invariant and non-invariant latent representations, then adaptively mixes the non-invariant branch to regularize environment-specific variation. On LIBERO-Plus it posts a 76.2% average zero-shot success rate versus 69.6% for OpenVLA-OFT, and on RoboTwin it lifts π0.5 clean-to-randomized success from 46.0% to 62.4%. Because it needs no extra out-of-distribution data or architecture changes, it is a practical robustness upgrade for existing VLA stacks.