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1. Theoretical Considerations for Social Learning between a Human Observer and a Robot Model

   Kim, B. ; Phillips, E. ( October 2021 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.
2. Exercising with Baxter: preliminary support for assistive social-physical human-robot interaction

   https://doi.org/10.1186/s12984-020-0642-5  

   Fitter, Naomi T. ; Mohan, Mayumi ; Kuchenbecker, Katherine J. ; Johnson, Michelle J. ( February 2020 , Journal of NeuroEngineering and Rehabilitation)

   The worldwide population of older adults will soon exceed the capacity of assisted living facilities. Accordingly, we aim to understand whether appropriately designed robots could help older adults stay active at home.

   Building on related literature as well as guidance from experts in game design, rehabilitation, and physical and occupational therapy, we developed eight human-robot exercise games for the Baxter Research Robot, six of which involve physical human-robot contact. After extensive iteration, these games were tested in an exploratory user study including 20 younger adult and 20 older adult users.

   Only socially and physically interactive games fell in the highest ranges for pleasantness, enjoyment, engagement, cognitive challenge, and energy level. Our games successfully spanned three different physical, cognitive, and temporal challenge levels. User trust and confidence in Baxter increased significantly between pre- and post-study assessments. Older adults experienced higher exercise, energy, and engagement levels than younger adults, and women rated the robot more highly than men on several survey questions.

   The results indicate that social-physical exercise with a robot is more pleasant, enjoyable, engaging, cognitively challenging, and energetic than similar interactions that lack physical touch. In addition to this main finding, researchers working in similar areas can build onmore »our design practices, our open-source resources, and the age-group and gender differences that we found.

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3. Building Language-Agnostic Grounded Language Learning Systems.

   https://doi.org/10.1109/RO-MAN46459.2019.8956449  

   Kery, Caroline ; Pillai, Nisha ; Matuszek, Cynthia ; Ferraro, Francis ( October 2019 , IEEE International Conference on Robot and Human Interactive Communication)

   Learning the meaning of grounded language---language that references a robot’s physical environment and perceptual data---is an important and increasingly widely studied problem in robotics and human-robot interaction. However, with a few exceptions, research in robotics has focused on learning groundings for a single natural language pertaining to rich perceptual data. We present experiments on taking an existing natural language grounding system designed for English and applying it to a novel multilingual corpus of descriptions of objects paired with RGB-D perceptual data. We demonstrate that this specific approach transfers well to different languages, but also present possible design constraints to consider for grounded language learning systems intended for robots that will function in a variety of linguistic settings.
4. What Happens When Robots Punish? Evaluating Human Task Performance During Robot-Initiated Punishment

   https://doi.org/10.1145/3472207  

   Jois, Himavath ; Wagner, Alan R. ( December 2021 , ACM Transactions on Human-Robot Interaction)

   This article examines how people respond to robot-administered verbal and physical punishments. Human participants were tasked with sorting colored chips under time pressure and were punished by a robot when they made mistakes, such as inaccurate sorting or sorting too slowly. Participants were either punished verbally by being told to stop sorting for a fixed time, or physically, by restraining their ability to sort with an in-house crafted robotic exoskeleton. Either a human experimenter or the robot exoskeleton administered punishments, with participant task performance and subjective perceptions of their interaction with the robot recorded. The results indicate that participants made more mistakes on the task when under the threat of robot-administered punishment. Participants also tended to comply with robot-administered punishments at a lesser rate than human-administered punishments, which suggests that humans may not afford a robot the social authority to administer punishments. This study also contributes to our understanding of compliance with a robot and whether people accept a robot’s authority to punish. The results may influence the design of robots placed in authoritative roles and promote discussion of the ethical ramifications of robot-administered punishment.
5. Learning Object Attributes with Category-Free Grounded Language from Deep Featurization

   Richards, Luke E. ; Darvish, Kasra ; Matuszek, Cynthia ( September 2020 , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   While grounded language learning, or learning the meaning of language with respect to the physical world in which a robot operates, is a major area in human-robot interaction studies, most research occurs in closed worlds or domain-constrained settings. We present a system in which language is grounded in visual percepts without using categorical constraints by combining CNN-based visual featurization with natural language labels. We demonstrate results comparable to those achieved using handcrafted features for specific traits, a step towards moving language grounding into the space of fully open world recognition.