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arXiv · 2106.15965

Embedded out-of-distribution detection on an autonomous robot platform

Michael Yuhas·Yeli Feng·Daniel Jun Xian Ng·Zahra Rahiminasab·Arvind Easwaran
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Abstract

Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs are not reliable when testing data are novel with regards to model training and validation data, i.e., out-of-distribution (OOD) test data. We implement an unsupervised deep neural network-based OOD detector on a real-time embedded autonomous Duckiebot and evaluate detection performance. Our OOD detector produces a success rate of 87.5% for emergency stopping a Duckiebot on a braking test bed we designed. We also provide case analysis on computing resource challenges specific to the Robot Operating System (ROS) middleware on the Duckiebot.

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Michael Yuhas, Yeli Feng, Daniel Jun Xian Ng, Zahra Rahiminasab, Arvind Easwaran. 2021-06-30. Embedded out-of-distribution detection on an autonomous robot platform. https://doi.org/10.1145/3445034.3460509

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