LIBERO-X: Robustness Litmus for Vision-Language-Action Models
Reliable benchmarking is critical for advancing Vision-Language-Action (VLA) models, as it reveals their generalization, robustness, and alignment of perception with language-driven manipulation tasks. However, existing benchmarks often provide limited or misleading assessments due to insufficient evaluation protocols that inadequately capture real-world distribution shifts. This work systematically rethinks VLA benchmarking from both evaluation and data perspectives, introducing LIBERO-X, a benchmark featuring: 1) A hierarchical evaluation protocol with progressive difficulty levels targeting three core capabilities: spatial generalization, object recognition, and task instruction unders...
| Authors | Guodong Wang, Chenkai Zhang, Qingjie Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 February 2026 |
| Last updated at source | 6 February 2026 |
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