SurgVLA-Bench: Towards Evaluating Vision-Language-Action Models for Laparoscopic Surgical Robotics
Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics. Despite the prevalence of VLA benchmarks for general robotics, standardized evaluation platforms specifically designed for surgical contexts remain absent. To address this limitation, we present SurgVLA-Bench, the first comprehensive benchmark for evaluating VLA models in laparoscopic surgical robotics. Leveraging the SurRoL simulation platform, we construct a hierarchical task taxonomy ranging from atomic actions to complete surgical procedures, complemented by a multi-dimension
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- LinkedLinked via unknownvlm-starter →
- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “SurgVLA-Bench: Towards Evaluating Vision-Language-Action Mod” ≈ “VioletVision-3B””
- LinkedLinked via unknownVG-GUI-TASKER/VG-GUI-TASKER →
- PossiblePossibly related (embedding) · 49%sou350121/VLA-Handbook →
- PossiblePossibly related (embedding) · 48%lucidrains/mimic-video →
- PossiblePossibly related (embedding) · 47%Video Friday: Your Robot Surgeon Will See You Now →
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “SurgVLA-Bench: Towards Evaluating Vision-Language-Action Mod” ≈ “pytorch/vision””
- FuzzySimilar title/name (fuzzy) · 84%liguodongiot/llm-action →
“Fuzzy title match (0.92): “SurgVLA-Bench: Towards Evaluating Vision-Language-Action Mod” ≈ “liguodongiot/llm-action””