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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. Inferring Human Intent and Predicting Human Action in Human–Robot Collaboration

   https://doi.org/10.1146/annurev-control-071223-105834  

   Hoffman, Guy; Bhattacharjee, Tapomayukh; Nikolaidis, Stefanos (July 2024, Annual Review of Control, Robotics, and Autonomous Systems)

   Researchers in human–robot collaboration have extensively studied methods for inferring human intentions and predicting their actions, as this is an important precursor for robots to provide useful assistance. We review contemporary methods for intention inference and human activity prediction. Our survey finds that intentions and goals are often inferred via Bayesian posterior estimation and Markov decision processes that model internal human states as unobserved variables or represent both agents in a shared probabilistic framework. An alternative approach is to use neural networks and other supervised learning approaches to directly map observable outcomes to intentions and to make predictions about future human activity based on past observations. That said, due to the complexity of human intentions, existing work usually reasons about limited domains, makes unrealistic simplifications about intentions, and is mostly constrained to short-term predictions. This state of the art provides opportunity for future research that could include more nuanced models of intents, reason over longer horizons, and account for the human tendency to adapt. 

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3. 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))
4. Exploring Human-Human Teaming and Human-Machine Interaction in a Collaborative Virtual Learning Factory

   Begum, Rumena; Aqlan, Faisal; Decaro, Marci S; Yang, Hui; Zhao, Richard; Saleem, Jason J; Murphy, P Karen (July 2025, American Society For Engineering Education)
5. Modeling and Analysis of Human Comfort in Human–Robot Collaboration

   https://doi.org/10.3390/biomimetics8060464  

   Yan, Yuchen; Su, Haotian; Jia, Yunyi (October 2023, Biomimetics)

   The emergence and recent development of collaborative robots have introduced a safer and more efficient human–robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, human intention detection, etc., while one significant factor—human comfort—has been frequently ignored. The comfort factor is critical to COBOT users due to its great impact on user acceptance. In previous studies, there is a lack of a mathematical-model-based approach to quantitatively describe and predict human comfort in HRC scenarios. Also, few studies have discussed the cases when multiple comfort factors take effect simultaneously. In this study, a multi-linear-regression-based general human comfort prediction model is proposed under human–robot collaboration scenarios, which is able to accurately predict the comfort levels of humans in multi-factor situations. The proposed method in this paper tackled these two gaps at the same time and also demonstrated the effectiveness of the approach with its high prediction accuracy. The overall average accuracy among all participants is 81.33%, while the overall maximum value is 88.94%, and the overall minimum value is 72.53%. The model uses subjective comfort rating feedback from human subjects as training and testing data. Experiments have been implemented, and the final results proved the effectiveness of the proposed approach in identifying human comfort levels in HRC. 

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