Demo Data from SIGIR 2026 / reviewers in the wild / expert
Haruyoshi Daitoh
dblp:86/1888
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
assistive robotics |
0.0 | 1 | 1999 | Construction of a human/robot coexistence system based on a model of human will-intention and desire · ICRA 1999 |
Human-robot interaction › long-term interaction
human-robot coexistence |
0.0 | 1 | 1999 | Construction of a human/robot coexistence system based on a model of human will-intention and desire · ICRA 1999 |
Human-robot interaction
intention recognition |
0.0 | 1 | 1999 | Construction of a human/robot coexistence system based on a model of human will-intention and desire · ICRA 1999 |
Methods — techniques the papers use, named apart from their topics
impedance control · 0.0hidden markov model · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Construction of a human/robot coexistence system based on a model of human will-intention and desireabstractWe propose a human-will-based approach to constructing a coexistence system in which a robot is assumed to assist the will power of a subject while reflecting his intention and desire. We discuss the model of human's will associated by the work - psychological mind reading, and identify the advantages of the system in which the robot guides the the subject manipulating along the path intended by him. Then, we describe two major techniques with experimental results: 1) estimation of the human-intended path pattern from the previously assigned one by proposing a modified algorithm of hidden Markov model; and 2) merging of the human's force feeling pattern into the desired one by proposing a field impedance equalizer which allows both the human to express his desirable field-dependent force/speed feeling and the robot to display the impedance along the previously estimated path. Finally, we conclude the study with some comments on future works. Yoji Yamada, Yoji Umetani, Haruyoshi Daitoh, Takayuki Sakai |
ICRA | 3 |