NeurIPS paper proves when RNN+MLP can provably recover true robot state
chriswolfvision · x · 2026-11-12
A NeurIPS 2026 paper, "Provable State Estimation with Recurrent Models," addresses a long-standing gap: RNNs are widely used for state estimation in robotics but lack convergence guarantees. The authors show conditions under which an RNN+MLP provably recovers the true state.
Per the figure: a contracting Neural ODE (ż = κgΩ(z,o)) processes observations, with a contraction property that exponentially forgets initial conditions; an MLP decoder's estimate enters an ε-neighborhood of the true state after finite observation time; an injective state-to-latent map is guaranteed (Theorems 3.4–3.7), and larger κ speeds convergence at the cost of network capacity (Theorem 4.2).
Related event: NeurIPS Paper Delivers First Provable Guarantees for RNN State Estimation(2 posts)→
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