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1. Theory of Mind Assessment with Human-Human and Human-Robot Interactions

   https://doi.org/10.1007/978-3-031-05409-9_41  

   Rabe, T. ; Callis, A. ; Zheng, Z. ; Heard, J. ; Bailey, R. ; Alm, C. ( June 2022 , Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science)

   Human-robot interaction has played an increasingly significant role in more recent research involving the Theory of Mind (ToM). As the use of robot facilitators increases, questions arise regarding the implications of their involvement in a research setting. This work addresses the effects of a humanoid robot facilitator in a ToM assessment. This paper analyzes subjects’ performances on tasks meant to test ToM as those tasks are delivered by human or robot facilitators. Various modalities of data were collected: performance on ToM tasks, subjects’ perceptions of the robot, results from a ToM survey, and response duration. This paper highlights the effects of human-robot interactions in ToM assessments, which ultimately leads to a discussion on the effectiveness of using robot facilitators in future human-subject research. 

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2. Emotionally Specific Backchanneling in Social Human-Robot Interaction and Human-Human Interaction

   https://doi.org/10.1109/IROS55552.2023.10341823  

   Shahverdi, Pourya ; Rousso, Katelyn ; Klotz, Justin ; Bakhoda, Iman ; Zribi, Malek ; Louie, Wing-Yue Geoffrey ( October 2023 , 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
3. Human Pose Estimation in UAV-Human Workspace

   https://doi.org/10.1007/978-3-030-90963-5_13  

   Wang, J. ( November 2021 , HCI International 2021)

   A 6D human pose estimation method is studied to assist autonomous UAV control in human environments. As autonomous robots/UAVs become increasingly prevalent in the future workspace, autonomous robots must detect/estimate human movement and predict their trajectory to plan a safe motion path. Our method utilize a deep Convolutional Neural Network to calculate a 3D torso bounding box to determine the location and orientation of human objects. The training uses a loss function that includes both 3D angle and translation errors. The trained model delivers <10-degree angular error and outperforms a reference method based on RSN. 

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4. Human Gaze Following for Human-Robot Interaction

   Saran, Akanksha ; Majumdar, Srinjoy ; Short, Elaine ; Thomaz, Andrea ; Niekum, Scott ( October 2018 , Proceedings of the International Conference on Intelligent Robots and Systems)
5. Construction Worker Workload Assessment for Human-Human versus Human-Robot Collaboration in Wood Assembly

   https://doi.org/10.1061/9780784485248.039  

   Okonkwo, Chinedu ; Liang, Xiaoyun ; Rasheed, Usman ; Awolusi, Ibukun ; Cai, Jiannan ; Wibranek, Bastian ( January 2024 , American Society of Civil Engineers)