Real-Time Synthesis of Robust Controlled Invariant Sets for Monotone Systems
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2606. 15594v1 Announce Type: cross 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.
Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware...
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.
The paper introduces A‑SLS, a learning‑aware robust adaptive model predictive control framework that forecasts future parameter uncertainty and preserves correlations among disturbances, parameter errors, and estimate updates to build tight reachable sets. It presents a GPU‑accelerated solver enabling real‑time performance on high‑dimensional systems, demonstrated on a quadruped with 61 states and a Crazyflie drone that learns dynamics online to stabilize during flight.
arXiv:2511. 05355v3 Announce Type: replace Abstract: Flow matching (FM) has shown promising results in data-driven planning.
arXiv:2606. 14536v1 Announce Type: new Abstract: Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints.