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1. Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI

   https://doi.org/10.3389/frobt.2021.683066  

   Spaulding, Samuel ; Shen, Jocelyn ; Park, Hae Won ; Breazeal, Cynthia ( June 2021 , Frontiers in Robotics and AI)

   null (Ed.)

   Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies. 

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2. Towards a Unified Planner For Socially-Aware Navigation

   Banisetty, S. ; Feil-Seifer, D. ( November 2018 , AAAI Fall Symposium Series: AI-HRI Artificial Intelligence for Human-Robot Interaction)

   This paper presents a novel architecture to attain a Unified Planner for Socially-aware Navigation (UP-SAN) and explains its need in Socially Assistive Robotics (SAR) applications. Our approach emphasizes interpersonal distance and how spatial communication can be used to build a unified planner for a human-robot collaborative environment. Socially-Aware Navigation (SAN) is vital to make humans feel comfortable and safe around robots, HRI studies have show that the importance of SAN transcendent safety and comfort. SAN plays a crucial role in perceived intelligence, sociability and social capacity of the robot thereby increasing the acceptance of the robots in public places. Human environments are very dynamic and pose serious social challenges to the robots indented for human interactions. For the robots to cope with the changing dynamics of a situation, there is a need to infer intent and detect changes in the interaction context. SAN has gained immense interest in the social robotics community; to the best of our knowledge, however, there is no planner that can adapt to different interaction contexts spontaneously after autonomously sensing that context. Most of the recent efforts involve social path planning for a single context. In this work, we propose a novel approach for a Unified Planner for SAN that can plan and execute trajectories that are human-friendly for an autonomously sensed interaction context. Our approach augments the navigation stack of Robot Operating System (ROS) utilizing machine learn- ing and optimization tools. We modified the ROS navigation stack using a machine learning-based context classifier and a PaCcET based local planner for us to achieve the goals of UP- SAN. We discuss our preliminary results and concrete plans on putting the pieces together in achieving UP-SAN. 

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3. Human-machine communication for assistive IoT technologies

   https://doi.org/16488612  

   Alexandra Porter, Md Muztoba ( October 2016 , 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS))

   Despite the phenomenal advances in the computational power and functionality of electronic systems, human-machine interaction has largely been limited to simple control panels, keyboard, mouse and display. Consequently, these systems either rely critically on close human guidance or operate almost independently from the user. An exemplar technology integrated tightly into our lives is the smartphone. However, the term “smart” is a misnomer, since it has fundamentally no intelligence to understand its user. The users still have to type, touch or speak (to some extent) to express their intentions in a form accessible to the phone. Hence, intelligent decision making is still almost entirely a human task. A life-changing experience can be achieved by transforming machines from passive tools to agents capable of understanding human physiology and what their user wants [1]. This can advance human capabilities in unimagined ways by building a symbiotic relationship to solve real world problems cooperatively. One of the high-impact application areas of this approach is assistive internet of things (IoT) technologies for physically challenged individuals. The Annual World Report on Disability reveals that 15% of the world population lives with disability, while 110 to 190 million of these people have difficulty in functioning [1]. Quality of life for this population can improve significantly if we can provide accessibility to smart devices, which provide sensory inputs and assist with everyday tasks. This work demonstrates that smart IoT devices open up the possibility to alleviate the burden on the user by equipping everyday objects, such as a wheelchair, with decision-making capabilities. Moving part of the intelligent decision making to smart IoT objects requires a robust mechanism for human-machine communication (HMC). To address this challenge, we present examples of multimodal HMC mechanisms, where the modalities are electroencephalogram (EEG), speech commands, and motion sensing. We also introduce an IoT co-simulation framework developed using a network simulator (OMNeT++) and a robot simulation platform Virtual Robot Experimentation Platform (V-REP). We show how this framework is used to evaluate the effectiveness of different HMC strategies using automated indoor navigation as a driver application. 

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4. Advancing Socially-Aware Navigation for Public Spaces

   https://doi.org/10.1109/RO-MAN53752.2022.9900653  

   Salek Shahrezaie, Roya ; Manalo, Bethany N. ; Brantley, Aaron G. ; Lynch, Casey R. ; Feil-Seifer, David ( August 2022 , IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots’ long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.Our prior Socially-Aware Navigation model considered con-text classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot’s navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment. 

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5. Impact of Interaction Context on the Student Affect-Learning Relationship in Child-Robot Interaction

   https://doi.org/10.1145/3319502.3374822  

   Chen, Huili ; Park, Hae Won ; Zhang, Xiajie ; Breazeal, Cynthia ( March 2020 , ACM/IEEE International Conference on Human-Robot Interaction)

   Prior work in affect-aware educational robots has often relied on a common belief that the relationship between student affect and learning is independent of agent behaviors (child’s/robot’s) or unidirectional (positive/negative but not both) throughout the entire student-robot interaction.We argue that the student affect-learning relationship should be interpreted in two contexts: (1) social learning paradigm and (2) sub-events within child-robot interaction. In our paper, we examine two different social learning paradigms where children interact with a robot that acts either as a tutor or a tutee. Sub-events within child-robot interaction are defined as task-related events occurring in specific phases of an interaction (e.g., when the child/robot gets a wrong answer). We examine subevents at a macro level (entire interaction) and a micro level (within specific sub-events). In this paper, we provide an in-depth correlation analysis of children’s facial affect and vocabulary learning. We found that children’s affective displays became more predictive of their vocabulary learning when children interacted with a tutee robot who did not scaffold their learning. Additionally, children’s affect displayed during micro-level events was more predictive of their learning than during macro-level events. Last, we found that the affect-learning relationship is not unidirectional, but rather is modulated by context, i.e., several affective states facilitated student learning when displayed in some sub-events but inhibited learning when displayed in others. These findings indicate that both social learning paradigm and sub-events within interaction modulate student affect-learning relationship. 

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