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researchsquare.com Nov 3, 2026
VLA action choice stops trusting the top score alone: FRS-Grounding adds neighborhood consistency for 86.4% simulated success
The highest-scoring action from a vision-language-action (VLA) model is not always the most reliable one to execute. FRS-Grounding keeps the finite candidate set after VLA inference, fuses normalized candidate scores with distance to a safe-action set into a fuzzy action set, and builds a pairwise relation from pose differences and collision-free states before fuzzy rough lower and upper approximations separate strongly supported candidates from boundary cases. Across 1500 randomized simulations the full pipeline reached 86.4% success and a 2.1% safety-violation rate, versus 74.8% and 8.2% for the pointwise hard-constraint baseline VLA-HC; under positional noise with σ = 10 mm it retained about 78% success against about 28% for VLA-HC.
The highest-scoring action from a vision-language-action (VLA) model is not always the most reliable one to execute. FRS-Grounding keeps the finite candidate set after VLA inference, fuses normalized candidate scores with distance to a safe-action set into a fuzzy action set, and builds a pairwise relation from pose differences and collision-free states before fuzzy rough lower and upper approximations separate strongly supported candidates from boundary cases. Across 1500 randomized simulations the full pipeline reached 86.4% success and a 2.1% safety-violation rate, versus 74.8% and 8.2% for the pointwise hard-constraint baseline VLA-HC; under positional noise with σ = 10 mm it retained about 78% success against about 28% for VLA-HC.
Jiangsu University of Science and Technology Yangzhou University
Mengqi Mao（First author）
FRS-Grounding Fuzzy Rough Sets Vision-Language-Action Models Robotic Manipulation
researchsquare.com Oct 19, 2026
Ground beef lymph-node adulteration moves past visual inspection: four-excitation fluorescence plus LSTM holds RPD 9.3 at unseen slaughterhouses
Once lymph-node tissue is mixed into finely comminuted ground beef, visual inspection can no longer find it, while DNA and protein methods are specific but destructive and need wet-chemistry preparation. Four-excitation front-face fluorescence spectroscopy with an explainable LSTM was trained on 824 samples from five slaughterhouses and three breeds, then tested on two unseen slaughterhouses, reaching R2P 0.988–0.993, RMSEP 2.60–3.35% w/w and RPD 9.3–12.0. An excitation ablation attributes the specificity to multi-excitation sampling rather than the network, with a 1.8% w/w detection limit and a 6.0% w/w quantification limit.
Once lymph-node tissue is mixed into finely comminuted ground beef, visual inspection can no longer find it, while DNA and protein methods are specific but destructive and need wet-chemistry preparation. Four-excitation front-face fluorescence spectroscopy with an explainable LSTM was trained on 824 samples from five slaughterhouses and three breeds, then tested on two unseen slaughterhouses, reaching R2P 0.988–0.993, RMSEP 2.60–3.35% w/w and RPD 9.3–12.0. An excitation ablation attributes the specificity to multi-excitation sampling rather than the network, with a 1.8% w/w detection limit and a 6.0% w/w quantification limit.
Yangzhou University Université d'Artois
Asima Saleemᵃ（First author）
Front-Face Fluorescence Spectroscopy Meat Adulteration Detection Long Short-Term Memory SHAP Explainable AI

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