Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC

• URL: http://arxiv.org/abs/2606.15594v1
• Date: Sun, 14 Jun 2026 04:34:03 GMT
• Title: Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC
• Abstract summary: We present SLS2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC)<n>Our approach trains an action-conditioned joint-embedding world model with compact Markovian latent states.<n>To enforce safety for the true system despite imperfect latent predictions, we inform a GPU-accelerated system level synthesis (SLS) robust MPC scheme.
• Score: 7.137568417435808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models. Our approach trains an action-conditioned joint-embedding world model with compact Markovian latent states, enabling efficient gradient-based trajectory optimization through learned latent dynamics. To enforce safety for the true system despite imperfect latent predictions, we inform a GPU-accelerated system level synthesis (SLS) robust MPC scheme with conformal prediction to obtain calibrated latent error bounds and robust latent-space constraint sets. We further learn and conformalize a latent constraint checker, allowing the SLS planner to impose probabilistic safety constraints during closed-loop execution. We evaluate our method on vision-based control tasks, where it improves both goal-reaching performance and safety over latent world-model and safe-planning baselines.

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