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1. Towards Meaningfully Integrating Human-Autonomy Teaming in Applied Settings

   https://doi.org/10.1145/3406499.3415077  

   Schelble, Beau G.; Flathmann, Christopher; McNeese, Nathan (November 2020, Proceedings of the 8th International Conference on Human-Agent Interaction)

   null (Ed.)

   Technological advancement goes hand in hand with economic advancement, meaning applied industries like manufacturing, medicine, and retail are set to leverage new practices like human-autonomy teams. These human-autonomy teams call for deep integration between artificial intelligence and the human workers that make up a majority of the workforce. This paper identifies the core principles of the human-autonomy teaming literature relevant to the integration of human-autonomy teams in applied contexts and research due to this large scale implementation of human-autonomy teams. A framework is built and defined from these fundamental concepts, with specific examples of its use in applied contexts and the interactions between various components of the framework. This framework can be utilized by practitioners of human-autonomy teams, allowing them to make informed decisions regarding the integration and training of human-autonomy teams. 

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2. A MultiModal Social Robot Toward Personalized Emotion Interaction

   Xie, Baijun Xie; Park, Chung Hyuk (November 2021, Artificial Intelligence for Human-Robot Interaction (AI-HRI) Fall Symposium)

   Human emotions are expressed through multiple modalities, including verbal and non-verbal information. Moreover, the affective states of human users can be the indicator for the level of engagement and successful interaction, suitable for the robot to use as a rewarding factor to optimize robotic behaviors through interaction. This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy and personalize emotional interaction for a human user. The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework. 

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3. A Trust-Assist Framework for Human–Robot Co-Carry Tasks

   https://doi.org/10.3390/robotics12020030  

   Hannum, Corey; Li, Rui; Wang, Weitian (April 2023, Robotics)

   Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework. 

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4. Designing Human-Autonomy Teaming Experiments Through Reinforcement Learning

   https://doi.org/10.1177/1071181320641340  

   Schelble, Beau; Canonico, Lorenzo-Barberis; McNeese, Nathan; Carroll, Jack; Hird, Casey (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   This paper creates and defines a framework for building and implementing human-autonomy teaming experiments that enable the utilization of modern reinforcement learning models. These models are used to train artificial agents to then interact alongside humans in a human-autonomy team. The framework was synthesized from experience gained redesigning a previously known and validated team task simulation environment known as NeoCITIES. Through this redesign, several important high-level distinctions were made that regarded both the artificial agent and the task simulation itself. The distinctions within the framework include gamification, access to high-performance computing, a proper reward function, an appropriate team task simulation, and customizability. This framework enables researchers to create experiments that are more usable for the human and more closely resemble real-world human-autonomy interactions. The framework also allows researchers to create veritable and robust experimental platforms meant to study human-autonomy teaming for years to come. 

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5. Stochastic-Skill-Level-Based Shared Control for Human Training in Urban Air Mobility Scenario

   https://doi.org/10.1145/3603194  

   Byeon, Sooyung; Choi, Joonwon; Zhang, Yutong; Hwang, Inseok (June 2023, ACM Transactions on Human-Robot Interaction)

   This paper proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices and provide personalized assistance based on the inferred SSLs. SSL can be assessed as a stochastic variable which denotes the probability that the novice will behave similarly to experts. We propose a data-driven method which can characterize novice demonstrations as a novice model and expert demonstrations as an expert model, respectively. Then, our SSL inference approach utilizes the novice and expert models to assess the SSL of the novices in complex dynamic control tasks. The shared control scheme is designed to dynamically adjust the level of assistance based on the inferred SSL to prevent frustration or tedium during human training due to poorly imposed assistance. The proposed framework is demonstrated by a human subject experiment in a human training scenario for a remotely piloted urban air mobility (UAM) vehicle. The results show that the proposed framework can assess the SSL and tailor the assistance for an individual in real-time. The proposed framework is compared to practice-only training (no assistance) and a baseline shared control approach to test the human learning rates in the designed training scenario with human subjects. A subjective survey is also examined to monitor the user experience of the proposed framework. 

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