開始時刻
前のセッションの直後
105. Human-Robot Interaction A
発表数
4
時間
2分30秒
発表担当
目次
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A Robot Jumping the Queue: Expectations About Politeness and Power During Conflicts in Everyday Human-Robot Encounters
説明
Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants' acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot's evaluation and effectiveness in everyday situations.
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I feel being there, they feel being together: Exploring How Telepresence Robots Facilitate Long-Distance Family Communication
説明
Many families often live geographically apart from each other due to work, education, or marriage. Therefore, long-distance families frequently use computer-mediated communication (CMC) tools to stay connected. While CMC tools have significantly improved family communication, they cannot fully mediate social presence. To examine the potential of telepresence robots for improving long-distance family communication, we conducted a two-week qualitative in situ study involving eight families. We analyzed recorded videos of their family interactions and conducted pre- and post-deployment interviews. Our findings highlight telepresence robots' potential as family communication tools, enabling immersive, natural, and dynamic interactions through physical embodiment and autonomy. Particularly, we identified five categories of family interaction mediated by telepresence robots: engaging in multi-party family communication, exploring home, restoring family routines, providing support, and having joint physical activities. Based on our findings, we present design guidelines for leveraging telepresence robots as effective family communication tools.
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PepperPose: Full-Body Pose Estimation with a Companion Robot
説明
Accurate full-body pose estimation across diverse actions in a user-friendly and location-agnostic manner paves the way for interactive applications in realms like sports, fitness, and healthcare. This task becomes challenging in real-world scenarios due to factors like the user's dynamic positioning, the diversity of actions, and the varying acceptability of the pose-capturing system. In this context, we present PepperPose, a novel companion robot system tailored for optimized pose estimation. Unlike traditional methods, PepperPose actively tracks the user and refines its viewpoint, facilitating enhanced pose accuracy across different locations and actions. This allows users to enjoy a seamless action-sensing experience. Our evaluation, involving 30 participants undertaking daily functioning and exercise actions in a home-like space, underscores the robot's promising capabilities. Moreover, we demonstrate the opportunities that PepperPose presents for human-robot interaction, its current limitations, and future developments.
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Giving Robots a Voice: Human-in-the-Loop Voice Creation and open-ended Labeling
説明
Speech is a natural interface for humans to interact with robots. Yet, aligning a robot's voice to its appearance is challenging due to the rich vocabulary of both modalities. Previous research has explored a few labels to describe robots and tested them on a limited number of robots and existing voices. Here, we develop a robot-voice creation tool followed by large-scale behavioral human experiments (N=2,505). First, participants collectively tune robotic voices to match 175 robot images using an adaptive human-in-the-loop pipeline. Then, participants describe their impression of the robot or their matched voice using another human-in-the-loop paradigm for open-ended labeling. The elicited taxonomy is then used to rate robot attributes and to predict the best voice for an unseen robot. We offer a web interface to aid engineers in customizing robot voices, demonstrating the synergy between cognitive science and machine learning for engineering tools.
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