SOURCE-LINKED INTELLIGENCE
LIBERO-VPro: Benchmarking Closed-Loop Visual Robustness of Robotic Foundation Models
Robotic foundation models achieve impressive performance on standard manipulation benchmarks, yet these evaluations typically assume clean, timely, and consistent visual observations throughout execution. We introduce LIBERO-VPro, a benchmark for systematically evaluating the closed-loop visual robustness of robotic foundation models by perturbing the visual evidence available during execution. LIBERO-VPro covers four complementary dimensions, including Visual Evidence Degradation, Camera Staleness, Visual Source Consistency, and Task-Relevant Scene Variation, spanning 12 challenge categories,
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T09:43:41.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.