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

Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks