Embedded out-of-distribution detection on an autonomous robot platform
arXiv:2106.15965 · doi:10.1145/3445034.3460509
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.
arXiv comment6 pages, 8 figures
References in corpus (6)
- Auto-Encoding Variational Bayes
- Out-of-distribution Detection in Classifiers via Generation
- Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
- Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
- Sim-to-real reinforcement learning applied to end-to-end vehicle control
- Anomalous Example Detection in Deep Learning: A Survey