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Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

We consider robotic motion planning and control under unknown dynamics with hybrid state observations, where state measurements are available only in parts of the state space. Existing work combines system identification, predicted reachability, graph search and controller synthesis in a hierarchical framework using local affine approximated models over polytopic state space partitioning, but requires state observations for identification and feedback control. Based on this framework, we address blind regions by selecting nominal dynamics and precomputing open-loop control sequences before obs

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