Congrats to our director and team on this amazing achievement. Proud to see AARC research making strides in #HumanRobotTeams and #AI innovation. #PurdueAARC
Exciting News! After 1.5 years of review, our paper titled “Cognitive Load-based Affective Workload Allocation for Multi-human Multi-robot Teams” has been accepted for publication in IEEE Transactions on Human-Machine Systems (#THMS)! This paper dives into the fields of #HumanMultiRobotSystems and #HumanRobotTeams, focusing on how humans and robots can collaborate more effectively as a team. We developed a system leveraging deep reinforcement learning (#DRL) to dynamically adjust workloads based on operator performance, combining both self-reported and real-time deep learning-based cognitive workload predictions. Our work included extensive real-world user studies in a CCTV monitoring scenario with multi-humans and multi-robots, demonstrating the proposed system's ability to dynamically adapt operators' cognitive workloads. Open-access preprint (arXiv): https://lnkd.in/gtCq68KZ Supplementary video: https://lnkd.in/g8Wpsb2u Paper website: https://lnkd.in/guqGKV4s We are deeply grateful to the reviewers and editors for their insightful?feedback and comments, which significantly improved our paper. A huge congratulations to my fantastic co-authors, Wonse Jo, Ruiqi Wang, Baijian Yang, Dan Foti, and Mo Rastgaar, and especially to our first author, Wonse, for his hard work and dedication to this exciting project. I’m so proud to see this research come to life! #HumanMachineSystems #MultiHumanMultiRobot #HumanRobotInteraction #AI #ReinforcementLearning #DeepLearning #NSFCAREER #Purdue #PurdueSMARTLab