VISION-SLS learns visual features with state-dependent error bounds and optimizes causal affine output-feedback policies via system level synthesis to achieve safe nonlinear control from RGB images.
Safe perception-based control under stochastic sensor uncertainty using con- formal prediction,
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UniDepthV2 predicts metric 3D points directly from single images using a self-promptable camera module, pseudo-spherical representation, and new losses for improved cross-domain generalization.
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VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis
VISION-SLS learns visual features with state-dependent error bounds and optimizes causal affine output-feedback policies via system level synthesis to achieve safe nonlinear control from RGB images.
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UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
UniDepthV2 predicts metric 3D points directly from single images using a self-promptable camera module, pseudo-spherical representation, and new losses for improved cross-domain generalization.