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This content will become publicly available on July 16, 2024

Title: A correct-and-certify approach to self-supervise object pose estimators via ensemble self-training
Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios. Modern learning-based approaches require large labeled datasets and tend to perform poorly outside the training domain. Our first contribution is to develop a robust corrector module that corrects pose estimates using depth information, thus enabling existing methods to better generalize to new test domains; the corrector operates on semantic keypoints (but is also applicable to other pose estimators) and is fully differentiable. Our second contribution is an ensemble self-training approach that simultaneously trains multiple pose estimators in a self-supervised manner. Our ensemble self-training architecture uses the robust corrector to refine the output of each pose estimator; then, it evaluates the quality of the outputs using observable correctness certificates; finally, it uses the observably correct outputs for further training, without requiring external supervision. As an additional contribution, we propose small improvements to a regression-based keypoint detection architecture, to enhance its robustness to outliers; these improvements include a robust pooling scheme and a robust centroid computation. Experiments on the YCBV and TLESS datasets show the proposed ensemble self-training outperforms fully supervised baselines while not requiring 3D annotations on real data.  more » « less
Award ID(s):
2044973
NSF-PAR ID:
10492598
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Robotics: Science and Systems (RSS)
Date Published:
Journal Name:
Robotics: Science and Systems (RSS)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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