  A key challenge towards the goal of multi-part assembly tasks is finding
robust sensorimotor control methods in the presence of uncertainty. In contrast
to previous works that rely on a priori knowledge on whether two parts match,
we aim to learn this through physical interaction. We propose a hierarchical
approach that enables a robot to autonomously assemble parts while being
uncertain about part types and positions. In particular, our probabilistic
approach learns a set of differentiable filters that leverage the tactile
sensorimotor trace from failed assembly attempts to update its belief about
part position and type. This enables a robot to overcome assembly failure. We
demonstrate the effectiveness of our approach on a set of object fitting tasks.
The experimental results indicate that our proposed approach achieves higher
precision in object position and type estimation, and accomplishes object
fitting tasks faster than baselines.
