Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks

A key challenge towards autonomous multi-part object assembly is robust sensorimotor control under 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...

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Bibliographic Details
Main Authors: Zachares, Peter A., Lee, Michelle A., Lian, Wenzhao, Bohg, Jeannette
Format: Conference Proceeding
Language:English
Subjects:
Automation
Conferences
Estimation
Fitting
Matched filters
Robot sensing systems
Uncertainty
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Summary:A key challenge towards autonomous multi-part object assembly is robust sensorimotor control under 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 show that the proposed approach achieves higher precision in object position and type estimation, and accomplishes object fitting tasks faster than baselines.
ISSN:2577-087X
DOI:10.1109/ICRA48506.2021.9560825