做机器人操作或VLA模型研究的团队，终于有了专门评估遮挡鲁棒性的基准和解决方案——VIM用视角想象补全感知，无需加摄像头就能提升性能，值得一试。
现有视觉-语言-动作（VLA）模型在标准操作基准上表现优异，但大多假设任务相关物体完全可见，这在现实场景中常因遮挡而失效。研究者提出LIBERO-Occ，一个基于LIBERO的遮挡扩展基准，发现当前最先进的VLA模型在遮挡下性能显著下降。为解决此问题，他们提出视角想象（VIM）方法，从遮挡的主视角生成互补视角，并基于观测和想象证据联合预测动作。VIM无需额外部署摄像头即可提升模型在多种任务、遮挡类型和严重程度下的鲁棒性。该基准和代码已开源。
LIBERO-Occ: Evaluating and Improving Vision-Language-Action Models under Scene-Induced Occlusion via Viewpoint Imagination
Vision-Language-Action (VLA) models achieve strong performance on standard manipulation benchmarks, but most evaluations assume that task-relevant objects are fully visible. This assumption often fails in realistic settings, where occlusion makes manipulation partially observable. In this paper, we study \textit{scene-induced occlusion} as a fundamental challenge for VLA models and introduce \textbf{LIBERO-Occ}, an occlusion-oriented extension of LIBERO. Experiments show that state-of-the-art VLAs suffer substantial performance degradation under occlusion. To address this issue, we propose \textbf{Viewpoint Imagination (VIM)}, which generates a complementary view from an occluded primary observation and conditions action prediction on both observed and imagined evidence. VIM improves robustness across task suites, occlusion types, and severity levels without requiring additional cameras at deployment time, suggesting that viewpoint imagination is an promising mechanism for perception completion in partially observable manipulation. Our benchmark and corresponding code are available at: \href{https://github.com/litsh/Libero-Occ}{https://github.com/litsh/Libero-Occ}.