A study of dialogue robots with haptic interactions

Ogawa, Takuki; Morita, Ken; Kitagawa, Hiroya; Fuketa, Masao; Aoe, Jun-ichi

doi:10.1109/nlpke.2011.6138210

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to solve the problem, this paper proposes a method of combination of DBC and TBC and an empathetic communication scheme for compact stuffed robots controlled by Internet servers [11,12]. In order to esti-mate empathetic communication schemes, the new empathy measurements for generalized conversations are defined.…”

Section: Introductionmentioning

confidence: 99%

A Method of Combination of Language Understanding with Touch-Based Communication Robots

Ogawa¹,

Bando²,

Morita³

et al. 2012

IJIS

Self Cite

View full text Add to dashboard Cite

Studies of robots which aim to entertain and to be conversational partners of the live-alone become very important. The robots are classified into DBC (Dialogue-Based Communication) robots and TBC (Touch-Based Communication) robots. DBC robots have an effect to be conversational partners. A typical application of TBC robots is Paro (a baby harp seal robot) which has an effect to entertain humans. The combination of DBC and TBC will be able to achieve both a conversational ability and an entertaining effect, but there is no study of combination of DBC and TBC. This paper proposes a response algorithm that can combine conversational information and touch information from humans. Criterions for estimation are defined as follows: FFV (Familiarity Factor Value), EFV (Enjoyment Factor Value), CR (Concentration Rate), ER (Expression Rate), and RR (Recognition Rate). FFV and EFV are total ratings for questionnaire related to familiarity and enjoyment factors, respectively. CR measures attention for humans. ER is the interest of communication with robots by representing Level 2 (laugh with opening one’s mouse), Level 1 (smile), and Level 0 (expressionless). RR is recognition ability for voices and touch actions. From the experiment for impressions of robot responses with 11 subjects, it turns out that the proposed method with combination of DBC and TBC is improved by 20.7 points in FFV, and by 12.6 points in EFV compared to only TBC. From the robot communication experiment, it turns out that the proposed method is improved by 8 points in the ER, by 5.3 points in the ER with Level 2, and by 24.5 points in the RR compared to only DBC

show abstract

Section: Introductionmentioning

confidence: 99%