Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Ryuki Matsuoka

dblp:404/5912 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
social robot
0.912025
Semantic Babbling: Interactive Baby Robot System Using Large Language Models · HRI 2025
Human-robot interaction › assistive robotics
therapeutic robot
0.312025
Semantic Babbling: Interactive Baby Robot System Using Large Language Models · HRI 2025

Methods — techniques the papers use, named apart from their topics

sentiment analysis · 0.9large language model · 0.9crowd-sourcing survey · 0.9
YearPublicationVenuePosition
2026 Voice Mitigates Expectation Deficiency: A Modality Comparison with Theory-Aligned Subjectivity Metrics in Co-Creative Image Generation
Ryuki Matsuoka, Michita Imai
ICAART (5)1
2025 Semantic Babbling: Interactive Baby Robot System Using Large Language Models
abstract
Therapeutic robots are used in facilities for older people, but many of them can only provide mechanical responses to users. To improve the quality of interactions, it is crucial to address emotional consistency. Thus, we propose Semantic Babbling, a system that enables interaction with users by utilizing the baby robot's “inner voice”. By generating inner voices and selecting babbling based on sentiment, Semantic Babbling aims to mimic infant-like responses. A crowd-sourcing survey demonstrated the significance of emotional consistency and the display of inner voice in Semantic Babbling, revealing that it improves the impression of baby robots.
Serina Miyake, Ryuki Matsuoka, Kohei Okuoka, Takuto Akiyoshi, Hidenobu Sumioka, Masahiro Shiomi, Michita Imai
HRI2