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1. Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

   Asano, Y.; Litman, D.; Yu, M.; Lobczowski, N. G.; Nokes-Malach, T.; Kovashka, A.; & Walker, E. (January 2022, Proceedings of the SIGdial 2022 Conference)

   Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work. 

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2. Power in Human-Robot Interaction

   Hou, Yoyo Tsung-Yu; Cheon, EunJeong; Jung, Malte F (March 2024, Florida family law set)
3. 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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4. A POMDP-based Robot-Human Trust Model for Human-Robot Collaboration

   Tilloo, Pallavi; Parron, Jesse; Obidat, Omar; Zhu, Michelle; Wang, Weitian (June 2022, The 12th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems)
5. Task Interdependence in Human-Robot Teaming

   Fangyun Zhao, Curt Henrichs (August 2020, IEEE International Conference on Robot and Human Interactive Communication)

   Human-robot teaming is becoming increasingly common within manufacturing processes. A key aspect practitioners need to decide on when developing effective processes is the level of task interdependence between human and robot team members. Task interdependence refers to the extent to which one’s behavior affects the performance of others in a team. In this work, we examine the effects of three levels of task interdependence—pooled, sequential, reciprocalin human-robot teaming on human worker’s mental states, task performance, and perceptions of the robot. Participants worked with the robot in an assembly task while their heart rate variability was being recorded. Results suggested human workers in the reciprocal interdependence level experienced less stress and perceived the robot more as a collaborator than other two levels. Task interdependence did not affect perceived safety. Our findings highlight the importance of considering task structure in human-robot teaming and inform future research on and industry practices for human-robot task allocation. 

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