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.
Planning with learned dynamics: Probabilis- tic guarantees on safety and reachability via lipschitz constants.IEEE Robotics and Automation Letters, 6(3): 5129–5136
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GPU-SLS computes safe robust nonlinear MPC policies online in ~20 ms for up to 75D systems by reachability-constrained system level synthesis accelerated via custom GPU QP solvers.
citing papers explorer
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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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Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
GPU-SLS computes safe robust nonlinear MPC policies online in ~20 ms for up to 75D systems by reachability-constrained system level synthesis accelerated via custom GPU QP solvers.