Emergent Mind
Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation
(2309.02525)
Published Sep 5, 2023
in
cs.RO
Abstract
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.
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