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DTSTART;TZID=America/New_York:20221013T133000
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UID:HFESAM_HFES 66th International Annual Meeting_sess176@linklings.com
SUMMARY:HART/HPM 6 - Modelling Human or AI Interactions in HARTs for Predi
 ctions and Training
DESCRIPTION:Lecture\n\nDeep Learning-based human activity recognition usin
 g RGB images in Human-robot collaboration\n\nKeshinro, Seong, Yi\n\nIn hum
 an-robot interaction, to ensure safety and effectiveness, robots need to b
 e able to accurately predict human intentions. Hidden Markov Model, Bayesi
 an Filtering, and deep learning methods have been used to predict human in
 tentions. However, few studies have explored deep learning methods to pr..
 .\n\n---------------------\nA Vision Inference Action Model For Shared Sit
 uational Awareness in Autonomous Agents: Approaches, Challenges, & Future 
 Directions\n\nKing, Hough, Collins\n\nAlthough there has been a recent inc
 rease of research investment in Human Autonomy Teams (HATs), many signific
 ant technical challenges and research gaps persist. Specifically, recent r
 eports indicate the need for shared situational awareness within HATs. Sha
 red situational awareness is a necessary p...\n\n---------------------\nTh
 e DataScope: A Mixed-Initiative Architecture for Data Labeling\n\nAlsaid, 
 Lee\n\nMachine learning promises many advantages, but achieving these prom
 ises requires methods that smooth the interaction between humans and machi
 ne learning. Machine learning systems require meticulous training on large
 , labeled datasets. Labeling data is a tedious expensive process, and many
  times requ...\n\n---------------------\nModeling Goal Alignment in Human-
 AI Teaming:  A Dynamic Game Theory Approach\n\nli, Lee\n\nAs human-AI team
 ing becomes increasingly prevalent, goal alignment has emerged as a critic
 al yet\nunsolved issue. Misaligned goals can be amplified by the situation
  and strategic interactions, which can\nfurther impact the teaming process
  and performance. These interrelated factors lack a systematic a...\n\n---
 ------------------\nMitigating the risk of musculoskeletal disorders durin
 g human robot collaboration: a reinforcement learning approach\n\nXie, Lu,
  Wang, Su, Liu...\n\nWork-related musculoskeletal disorders (MSDs) are oft
 en observed in human-robot collaboration (HRC), a common work configuratio
 n in modern factories. In this study, we aim to reduce the risk of MSDs in
  HRC scenarios by developing a novel model-free reinforcement learning (RL
 ) method to improve worke...\n\n\nTrack: Human AI Robot Teaming (HART), Hu
 man Performance Modeling
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