AI RESEARCH
VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis
arXiv CS.LG
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ArXi:2604.24894v1 Announce Type: cross We propose VISION-SLS, a method for nonlinear output-feedback control from high-resolution RGB images which provides robust constraint satisfaction guarantees under calibrated uncertainty bounds despite partial observability, sensor noise, and nonlinear dynamics. To enable scalability while retaining guarantees, we propose: (i) a learned low-dimensional observation map from pretrained visual features with state-dependent error bounds, and (ii) a causal affine time-varying output-feedback policy optimized via System Level Synthesis.