Speech driven trunk motion generating system based on physical constraint

Sakai, Kurima; Minato, Takashi; Ishi, Carlos Toshinori; Ishiguro, Hiroshi

doi:10.1109/roman.2016.7745136

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…were randomized during each robot action to make her more animated. The robot's lip and trunk movements were automatically generated in synchrony with the speaking rhythm based on the audio output from the speech synthesizer [57]. Interactions were recorded using three 1080p high resolution webcams and a microphone for the participant's speech, and the android's speech stream was recorded directly to file.…”

Section: Methodsmentioning

confidence: 99%

Modeling Interaction Structure for Robot Imitation Learning of Human Social Behavior

Doering

Glas

Ishiguro

2019

IEEE Trans. Human-Mach. Syst.

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This study presents a learning-by-imitation technique that learns social robot interaction behaviors from natural humanhuman interaction data and requires minimum input from a designer. To solve the problem of responding to ambiguous human actions, a novel topic clustering algorithm based on action cooccurrence frequencies is introduced. The system learns humanreadable rules that dictate which action the robot should take, based on the most recent human action and the current estimated topic of conversation. The technique is demonstrated in a scenario where the robot learns to play the role of a travel agent. The proposed technique outperformed several baseline techniques in qualitative and quantitative evaluations. It responded more accurately to ambiguous questions and participants found it was easier to understand, provided more information, and required less effort to interact with.

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Section: Methodsmentioning

confidence: 99%

Modeling Interaction Structure for Robot Imitation Learning of Human Social Behavior

Doering

Glas

Ishiguro

2019

IEEE Trans. Human-Mach. Syst.

Self Cite

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show abstract

“…When the android speaks, it moves its lips, head, and torso in synchronization with the prosodic features of its voice. Those movements are automatically generated based on an existing system developed by Ishi et al ( 2012 ) and Sakai et al ( 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Who Is Responsible for a Dialogue Breakdown? An Error Recovery Strategy That Promotes Cooperative Intentions From Humans by Mutual Attribution of Responsibility in Human-Robot Dialogues

et al. 2019

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We propose a strategy with which conversational android robots can handle dialogue breakdowns. For smooth human-robot conversations, we must not only improve a robot's dialogue capability but also elicit cooperative intentions from users for avoiding and recovering from dialogue breakdowns. A cooperative intention can be encouraged if users recognize their own responsibility for breakdowns. If the robot always blames users, however, they will quickly become less cooperative and lose their motivation to continue a discussion. This paper hypothesizes that for smooth dialogues, the robot and the users must share the responsibility based on psychological reciprocity. In other words, the robot should alternately attribute the responsibility to itself and to the users. We proposed a dialogue strategy for recovering from dialogue breakdowns based on the hypothesis and experimentally verified it with an android. The experimental result shows that the proposed method made the participants aware of their share of the responsibility of the dialogue breakdowns without reducing their motivation, even though the number of dialogue breakdowns was not statistically reduced compared with a control condition. This suggests that the proposed method effectively elicited cooperative intentions from users during dialogues.

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“…1 When ERICA is speaking, it moves its lips, head, and torso in synchrony with its voice's prosodic features. The lips, head, and torso movements are automatically generated from its voice using previously developed systems [10,16]. It randomly blinks its eyes.…”

Section: Robotmentioning

confidence: 99%

Female-Type Android’s Drive to Quickly Understand a User’s Concept of Preferences Stimulates Dialogue Satisfaction: Dialogue Strategies for Modeling User’s Concept of Preferences

Uchida

Minato

Nakamura

et al. 2021

Int J of Soc Robotics

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This research develops a conversational robot that stimulates users’ dialogue satisfaction and motivation in non-task-oriented dialogues that include opinion and/or preference exchanges. One way to improve user satisfaction and motivation is by demonstrating the robot’s ability to understand user opinions. In this paper, we explore a method that efficiently obtains the concept of user preferences: likes and dislikes. The concept is acquired by complementing a small amount of user preference data observed in dialogues. As a method for efficient collection, we propose a dialogue strategy that creates utterances with the largest expected complementation. Our experimental results with a female-type android robot suggest that the proposed strategy efficiently obtained user preferences and enhanced dialogue satisfaction. In addition, the strength of user motivation (i.e., long-term willingness to communicate with the android) is only positively correlated with the android’s willingness to understand. Our results not only show the effectiveness of our proposed strategy but also suggest a design theory for dialogue robots to stimulate dialogue motivation, although the current results are derived only from a female-type android.

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Speech driven trunk motion generating system based on physical constraint

Cited by 17 publications

References 28 publications

Modeling Interaction Structure for Robot Imitation Learning of Human Social Behavior

Modeling Interaction Structure for Robot Imitation Learning of Human Social Behavior

Who Is Responsible for a Dialogue Breakdown? An Error Recovery Strategy That Promotes Cooperative Intentions From Humans by Mutual Attribution of Responsibility in Human-Robot Dialogues

Female-Type Android’s Drive to Quickly Understand a User’s Concept of Preferences Stimulates Dialogue Satisfaction: Dialogue Strategies for Modeling User’s Concept of Preferences

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