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.

翻译：我们考虑了在环中使用增量不可微优化器学习机器人状态估计观测模型的问题。收敛到正确的机器人状态信念在很大程度上取决于对作为优化器输入的观测模型进行恰当调整。我们提出了一种基于梯度的学习方法，相较于现有最优方法，该方法能更快地收敛到模型估计值，从而在未见过的机器人测试轨迹跟踪精度上获得质量更好的解。