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   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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2. 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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   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. Modeling Human Temporal Uncertainty in Human-Agent Teams

   Abo Dominguez, Maya; La, William; Boerkoel, James C. (January 2020, Proceedings of the AI-HRI Symposium at AAAI-FSS 2020)

   null (Ed.)

   Automated scheduling is potentially a very useful tool for facilitating efficient, intuitive interactions between a robot and a human teammate. However, a current gap in automated scheduling is that it is not well understood how to best represent the timing uncertainty that human teammates introduce. This paper attempts to address this gap by designing an online human-robot collaborative packaging game that we use to build a model of human timing uncertainty from a population of crowdworkers. We conclude that heavy-tailed distributions are the best models of human temporal uncertainty, with a Log-Normal distribution achieving the best fit to our experimental data. We discuss how these results along with our collaborative online game will inform and facilitate future explorations into scheduling for improved human-robot fluency. 

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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)