Robot Health Indicator: A Visual Cue to Improve Level of Autonomy Switching Systems

Using different Levels of Autonomy (LoA), a human operator can vary the extent of control they have over a robot's actions. LoAs enable operators to mitigate a robot's performance degradation or limitations in the its autonomous capabilities. However, LoA regulation and other tasks may often overload an operator's cognitive abilities. Inspired by video game user interfaces, we study if adding a 'Robot Health Bar' to the robot control UI can reduce the cognitive demand and perceptual effort required for LoA regulation while promoting trust and transparency. This Health Bar uses the robot vitals and robot health framework to quantify and present runtime performance degradation in robots. Results from our pilot study indicate that when using a health bar, operators used to manual control more to minimise the risk of robot failure during high performance degradation. It also gave us insights and lessons to inform subsequent experiments on human-robot teaming.

Detection and Prevention Against Poisoning Attacks in Federated Learning

This paper proposes and investigates a new approach for detecting and preventing several different types of poisoning attacks from affecting a centralized Federated Learning model via average accuracy deviation detection (AADD). By comparing each client's accuracy to all clients' average accuracy, AADD detect clients with an accuracy deviation. The implementation is further able to blacklist clients that are considered poisoned, securing the global model from being affected by the poisoned nodes. The proposed implementation shows promising results in detecting poisoned clients and preventing the global model's accuracy from deteriorating.