Learning Adaptive Safety Margins for Visual Navigation
Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias. Diffusion-based planners propose diverse trajectory candidates from egocentric RGB-D, yet reliable selection remains the bottleneck. We propose a context-conditioned safety critic that learns an adaptive clearance preference for ranking diffusion proposals, decomposed into three complementary terms: (i) a safety term
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Learning Adaptive Safety Margins for Visual Navigation” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Junyi Hu →
“Learning Adaptive Safety Margins for Visual Navigation”
- LinkedLinked via arxiv author · 85%Shuaihang Yuan →
“Learning Adaptive Safety Margins for Visual Navigation”
- LinkedLinked via arxiv author · 85%Geeta Chandra Raju Bethala →
“Learning Adaptive Safety Margins for Visual Navigation”
- LinkedLinked via arxiv author · 85%Anthony Tzes →
“Learning Adaptive Safety Margins for Visual Navigation”
- LinkedLinked via arxiv author · 85%Yi Fang →
“Learning Adaptive Safety Margins for Visual Navigation”