VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis
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 (SLS). We develop a scalable, novel solver for the resulting nonconvex program that leverages sequential convex programming coupled with efficient Riccati recur...
| Authors | Antoine P. Leeman, Shuyu Zhan, Melanie N. Zeilinger et al. |
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| Primary category | cs.RO |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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