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See Yourself in Others: Attending Multiple Tasks for Own Failure Detection

Publication date:

  • February 2022

Authors:

Boyang Sun
Jiaxu Xing
Hermann Blum
Roland Siegwart
Cesar Cadena

Abstract

Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth estimation and normal estimation. These different tasks provide rich information for the whole robotic perception system. All tasks have their own characteristics while sharing some latent correlations. However, some of the task predictions may suffer from the unreliability dealing with complex scenes and anomalies. We propose an attention-based failure detection approach by exploiting the correlations among multiple tasks. The proposed framework infers task failures by evaluating the individual prediction, across multiple visual perception tasks for different regions in an image. The formulation of the evaluations is based on an attention network supervised by multi-task uncertainty estimation and their corresponding prediction errors. Our proposed framework generates more accurate estimations of the prediction error for the different task’s predictions.

Reference
Doi: 10.48550/arXiv.2110.02549
Published in: International Conference on Robotics and Automation (ICRA),
 Pages 1-8
Read the paper
Version: Published
Access: Open Access
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