Assessing Emotions in Human-Robot Interaction Based on the Appraisal Theory

Demutti, Marco; D’Amato, Vincenzo; Recchiuto, Carmine Tommaso; Oneto, Luca; Sgorbissa, Antonio

doi:10.1109/ro-man53752.2022.9900598

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Based on insights from appraisal theory [3,4], social signal processing approaches [2,8], as well as our field observations, we made the assumption that emotion and attention serve as important intermediate features for detecting perceived appropriateness. Based on our previous observations of factors that affect perceived appropriateness, and taking into account realistic robot behavior in a narrow passageway, we chose 8 robot behavior (sudden accelerations and stop, sudden direction changes, block, squeeze, and non-moving) to trigger a rich set of perceived appropriateness of robot social navigation behavior.…”

Section: Data Collection and Analysis: Detecting Perceived Appropriat...mentioning

confidence: 99%

Perceived Appropriateness

Zhou

2023

Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

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Robots navigating in social environments inevitably exhibit behavior perceived as inappropriate by people, which they will repeat unless they are aware of them; hindering their social acceptance. This highlights the importance of robots detecting and adapting to the perceived appropriateness of their behavior, in line with what we found in a systematic literature review. Therefore, we have conducted experiments (both outdoor and indoor) to understand the perceived appropriateness of robot social navigation behavior, based on which we collected a dataset and developed a machine learning model for detecting such perceived appropriateness. To investigate the usefulness of such information and inspire robot adaptive navigation behavior design, we will further conduct a WoZ study to understand how trained human operators adapt robot behavior to people's feedback. In all, this work will enable robots to better remediate their inappropriate behavior, thus improving their social acceptance. CCS CONCEPTS• Human-centered computing → Social navigation; Empirical studies in interaction design; Interaction design process and methods;• Computing methodologies → Cognitive robotics.

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Section: Data Collection and Analysis: Detecting Perceived Appropriat...mentioning

confidence: 99%

Perceived Appropriateness

Zhou

2023

Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

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“…Emotion is a fundamental factor in HRI that affects people’s attitudes and influences their decisions, actions, learning, communication, and situation awareness ( Poria et al, 2017 ; Demutti et al, 2022 ). If a person has a strong ability to observe others’ emotions and manage their own emotions, they are likely to contribute more successfully to the interaction with others ( Poria et al, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

Invoking and identifying task-oriented interlocutor confusion in human-robot interaction

Li,

Ross

2023

Front. Robot. AI

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Successful conversational interaction with a social robot requires not only an assessment of a user’s contribution to an interaction, but also awareness of their emotional and attitudinal states as the interaction unfolds. To this end, our research aims to systematically trigger, but then interpret human behaviors to track different states of potential user confusion in interaction so that systems can be primed to adjust their policies in light of users entering confusion states. In this paper, we present a detailed human-robot interaction study to prompt, investigate, and eventually detect confusion states in users. The study itself employs a Wizard-of-Oz (WoZ) style design with a Pepper robot to prompt confusion states for task-oriented dialogues in a well-defined manner. The data collected from 81 participants includes audio and visual data, from both the robot’s perspective and the environment, as well as participant survey data. From these data, we evaluated the correlations of induced confusion conditions with multimodal data, including eye gaze estimation, head pose estimation, facial emotion detection, silence duration time, and user speech analysis—including emotion and pitch analysis. Analysis shows significant differences of participants’ behaviors in states of confusion based on these signals, as well as a strong correlation between confusion conditions and participants own self-reported confusion scores. The paper establishes strong correlations between confusion levels and these observable features, and lays the ground or a more complete social and affect oriented strategy for task-oriented human-robot interaction. The contributions of this paper include the methodology applied, dataset, and our systematic analysis.

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