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Fall 2022 GRASP on Robotics: Julie Adams, Oregon State University, “Towards Adaptive Human-Robot Teams: Workload Estimation”
November 18, 2022 @ 10:30 am - 11:45 am
This was a hybrid event with in-person attendance in Wu and Chen and virtual attendance…
The ability for robots, be it a single robot, multiple robots or a robot swarm, to adapt to the humans with which they are teamed requires algorithms that allow robots to detect human performance in real time. The multi-dimensional workload algorithm incorporates physiological metrics to estimate overall workload and its components (i.e., cognitive, speech, auditory, visual and physical). The algorithm is sensitive to changes in a human’s individual workload components and overall workload across domains, human-robot teaming relationships (i.e., supervisory, peer-based), and individual differences. The algorithm has also been demonstrated to detect shifts in workload in real-time in order to adapt the robot’s interaction with the human and autonomously change task responsibilities when the human’s workload is over- or underloaded. Recently, the algorithm was used to post-hoc analyze the resulting workload for a single human deploying a heterogeneous robot swarm in an urban environment. Current efforts are focusing on predicting the human’s future workload, recognizing the human’s current tasks, and estimating workload for previously unseen tasks.