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

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