Author

Bin Wang

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Preprint Oct 2026

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

Extensive experiments across challenging manipulation settings, including LIBERO, LIBERO-Plus, the RoboTwin perturbation suite, and real-world tasks, demonstrate that MixVLA improves overall zero-shot robustness while retaining strong in-domain performance.

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