# Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation

arXiv: https://arxiv.org/abs/2309.02525 · HTML: https://mlsanity.com/paper/2309.02525/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2023.

## Built on by (most cited first, 2 of 2)

- [Your Learned Constraint is Secretly a Backward Reachable Tube](https://mlsanity.com/paper/2501.15618/) (2025)
- [Acoustic Neural 3D Reconstruction Under Pose Drift](https://mlsanity.com/paper/2503.08930/) (2025)

## Abstract (arXiv)

We consider the problem of learning observation models for robot state estimation with incremental non-differentiable optimizers in the loop. Convergence to the correct belief over the robot state is heavily dependent on a proper tuning of observation models which serve as input to the optimizer. We propose a gradient-based learning method which converges much quicker to model estimates that lead to solutions of much better quality compared to an existing state-of-the-art method as measured by the tracking accuracy over unseen robot test trajectories.