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Multi-task missions for unmanned aerial vehicles (UAVs) involving inspectionand landing tasks are challenging for novice pilots due to the difficultiesassociated with depth perception and the control interface. We propose a sharedautonomy system, alongside supplementary information displays, to assist pilotsto successfully complete multi-task missions without any pilot training. Ourapproach comprises of three modules: (1) a perception module that encodesvisual information onto a latent representation, (2) a policy module thataugments pilot's actions, and (3) an information augmentation module thatprovides additional information to the pilot. The policy module is trained insimulation with simulated users and transferred to the real world withoutmodification in a user study (n=29), alongside supplementary informationschemes including learnt red/green light feedback cues and an augmented realitydisplay. The pilot's intent is unknown to the policy module and is inferredfrom the pilot's input and UAV's states. The assistant increased task successrate for the landing and inspection tasks from [16.67% & 54.29%] respectivelyto [95.59% & 96.22%]. With the assistant, inexperienced pilots achieved similarperformance to experienced pilots. Red/green light feedback cues reduced therequired time by 19.53% and trajectory length by 17.86% for the inspectiontask, where participants rated it as their preferred condition due to theintuitive interface and providing reassurance. This work demonstrates thatsimple user models can train shared autonomy systems in simulation, andtransfer to physical tasks to estimate user intent and provide effectiveassistance and information to the pilot.

Learning to Assist and Communicate with Novice Drone Pilots for Expert Level Performance