A certified gradient-based method for contact-rich manipulation that quantifies smoothing-induced errors via set-valued discrepancies and incorporates them into analytical reachable sets for robust affine feedback policies.
Statistical safety and robustness guarantees for feedback motion planning of unknown underactuated stochastic systems
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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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Certified Gradient-Based Contact-Rich Manipulation via Smoothing-Error Reachable Tubes
A certified gradient-based method for contact-rich manipulation that quantifies smoothing-induced errors via set-valued discrepancies and incorporates them into analytical reachable sets for robust affine feedback policies.
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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.