Author

Bin Wang

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Preprint Oct 2026

MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models

Vision-Language-Action (VLA) models have achieved remarkable advances in robotic manipulation, yet their zero-shot generalization under out-of-distribution (OOD) conditions remains limited. These models often entangle task-relevant invariant structure with environment-specific non-invariant factors, causing policies to...

Ping-Rui Zhang, Yu Zhang, Peng-Yuan Wu et al. · 0 citations

Preprint Aug 2026

Dream2Reward: Transition-Alignment Reward Models from Positive Demonstrations for Robotic Manipulation

This work introduces Dream2Reward, which learns a language-conditioned successful latent transition field from positive demonstrations that provides stronger success-failure separation and more informative feedback on low-quality behavior than progress-based alternatives.

Hao-Yu Zhang, Ze-Cui Zeng, Bin Wang et al. · 3 citations