← 研踪 PaperTrailCVF · CVPR 2026:cvf:5b4e8a2e2d91deeebddd27123297888c · 记录 #1
LIBERO-Plus: A Progressive Robustness Benchmark for Visual-Language-Action Models
Fei, SenyuWang, SiyinShi, JunhaoDai, ZihaoCai, JikunQian, PengfangJi, LiHe, XinzheZhang, ShiduoFei, ZhaoyeFu, JinlanGong, JingjingQiu, Xipeng 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 22:46
摘要
Visual-Language-Action (VLA) models report impressive success rates exceeding 95% on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. Current simulation-based robustness evaluations suffer from narrow perturbation coverage, manual design constraints, and coarse-grained analysis that fails to reveal when and how models fail. To address this gap, we propose LIBERO-Plus, a comprehensive, automatic, and fine-grained evaluation framework with controlled perturbations across seven dimensions: object layouts, camera viewpoints, robot initial states, language instructions, lighting conditions, background textures, and sensor noise. Our systematic analysis of ten state-of-the-art models reveals consistent brittleness beneath apparent competence, with performance dropping from 95% to below 30% under modest perturbations. Our findings challenge the assumption that high benchmark scores equate to true competency and highlight the need for evaluation practices that assess reliability under realistic variation.
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记录 #1 · 2026 · 最新观测版本
LIBERO-Plus: A Progressive Robustness Benchmark for Visual-Language-Action Models
Visual-Language-Action (VLA) models report impressive success rates exceeding 95% on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. Current simulation-based robustness evaluations suffer from narrow perturbation coverage, manual design constraints, and coarse-grained analysis that fails to reveal when and how models fail. To address this gap, we propose LIBERO-Plus, a comprehensive, automatic, and fine-grained evaluation framework with controlled perturbations across seven dimensions: object layouts, camera viewpoints, robot initial states, language instructions, lighting conditions, background textures, and sensor noise. Our systematic analysis of ten state-of-the-art models reveals consistent brittleness beneath apparent competence, with performance dropping from 95% to below 30% under modest perturbations. Our findings challenge the assumption that high benchmark scores equate to true competency and highlight the need for evaluation practices that assess reliability under realistic variation.
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