A Robot-Based Cognitive Assessment Model Based on Visual Working Memory and Attention Level

Sharifara, Ali; Babu, Ashwin Ramesh; Rajavenkatanarayanan, Akilesh; Collander, Christopher; Makedon, Fillia

doi:10.1007/978-3-319-92049-8_43

Cited by 3 publications

(1 citation statement)

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“…Beyond focusing on specific content of speech, conversational robots can further affect the user's mental state in the way they speak. Robots can perform back-channelling to give the appearance of active listening (Birnbaum et al, 2016;Sebo et al, 2020), or give informative feedback to improve task performance (Guneysu and Arnrich, 2017;Law et al, 2017;Sharifara et al, 2018), a user's self-efficacy (Zafari et al, 2019), or their motivation (Mucchiani et al, 2017;Shao et al, 2019). Robots can choose to only interrupt a distracted user at appropriate times (Sirithunge et al, 2018;Unhelkar et al, 2020).…”

Section: Human Brainmentioning

confidence: 99%

Helping People Through Space and Time: Assistance as a Perspective on Human-Robot Interaction

2022

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As assistive robotics has expanded to many task domains, comparing assistive strategies among the varieties of research becomes increasingly difficult. To begin to unify the disparate domains into a more general theory of assistance, we present a definition of assistance, a survey of existing work, and three key design axes that occur in many domains and benefit from the examination of assistance as a whole. We first define an assistance perspective that focuses on understanding a robot that is in control of its actions but subordinate to a user’s goals. Next, we use this perspective to explore design axes that arise from the problem of assistance more generally and explore how these axes have comparable trade-offs across many domains. We investigate how the assistive robot handles other people in the interaction, how the robot design can operate in a variety of action spaces to enact similar goals, and how assistive robots can vary the timing of their actions relative to the user’s behavior. While these axes are by no means comprehensive, we propose them as useful tools for unifying assistance research across domains and as examples of how taking a broader perspective on assistance enables more cross-domain theorizing about assistance.

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