Using Visual Anomaly Detection for Task Execution Monitoring
Santosh Thoduka,
Juergen Gall,
Paul G. Plöger
Abstract:Execution monitoring is essential for robots to detect and respond to failures. Since it is impossible to enumerate all failures for a given task, we learn from successful executions of the task to detect visual anomalies during runtime. Our method learns to predict the motions that occur during the nominal execution of a task, including camera and robot body motion. A probabilistic U-Net architecture is used to learn to predict optical flow, and the robot's kinematics and 3D model are used to model camera and… Show more
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