Dear all, Our next AI seminar is scheduled to be on Jun 07th (Friday), 2-3 PM. Seminar Location: BEXL 320 Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkdz09<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonstate.zoom.us%2Fj%2F91611213801%3Fpwd%3DWm9JSkN1eW84RUpiS2JEd0E5TEVkdz09&data=05%7C02%7Cai%40engr.orst.edu%7C6591c72198b742d1920508dc8586c40f%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638532061057029078%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=tKbUYCtU6PRc%2BmCvMkIVxQee8FHiOlHHN0rrseFZKgQ%3D&reserved=0> Adaptive Workload Modeling for Human-Robot Teams Joshua Bhagat Smith Ph.D. Student Robotics and AI Oregon State University Abstract: Real world human-robot teams will be deployed in dynamic, uncertain environments where effective collaboration hinges on the robot's ability to comprehend its human teammate. Robots must have a dynamic awareness of the human’s internal state in order to adapt their behavior and provide appropriate assistance. A critical element of this adaptation is the ability to estimate the human's workload in unknown situations, as many real-world teams may be assigned tasks they have not be trained to perform. In this talk I will present 1) A multi-dimensional workload estimation algorithm capable of estimating all workload components, 2) a meta-learning based approach that adapts a machine learning model's parameters, such that workload can be accurately estimated for both known and unknown tasks, and 3) discuss the system considerations for a executing these algorithms in real-time on-board a robot. Speaker Bio: Joshua Bhagat Smith is a fourth year Robotics & AI dual major Ph.D. student supervised by Julie A. Adams. Prior to grad school he worked in industry for three years developing large-scale sensor processing systems to build maps for autonomous vehicles. He received his Master's in Computer Science and Bachelor's in Computer Engineering from the University of Arkansas, in 2015 and 2017 respectively. His current research interests include human-robot interaction and multi-robot systems, focusing on developing machine learning-based systems for real world human-robot teams. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineering.oregonstate.edu%2FEECS%2Fresearch%2FAI&data=05%7C02%7Cai%40engr.orst.edu%7C6591c72198b742d1920508dc8586c40f%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638532061057185339%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=6jaYFG9lzJw07vDwOnZpfRP9nHekkDbODeMZqOl%2BHdE%3D&reserved=0> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged