Simultaneous Dialog Robot System

Iwamoto, Tatsuya; Nishi, Kotaro; Unokuchi, Taishi

doi:10.1007/978-3-030-23528-4_15

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Robots explicitly developed to handle multi-party interactions include a bartender robot [15], a caretaker robot [26], a guide robot [28], and an embodied conversational agent [4]. These works and others often focus on a robot's ability to handle a specific aspect of multi-party interactions: receiving and responding to multiple requests [15,26], group detection [28,29], speech recognition [10,11], gesture generation [18], body orientation generation [30], gaze generation [4], etc. Relevant studies in multi-party turn-taking [3,14] use hand-crafted features (e.g., whether someone is speaking, head pose, prosody) to determine when the robot should take a turn, but do not incorporate the contents of speech.…”

Section: Related Workmentioning

confidence: 99%

Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data

Nanavati

Doering

Brščić

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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We envision a future where service robots autonomously learn how to interact with humans directly from human-human interaction data, without any manual intervention. In this paper, we present a data-driven pipeline that: (1) takes in low-level data of a human shopkeeper interacting with multiple customers (28 hours of collected data); (2) autonomously extracts high-level actions from that data; and (3) learns-without manual intervention-how a robotic shopkeeper should respond to customers' actions online. Our proposed system for learning the interaction logic uses neural networks to first learn which customer actions are important to respond to and then learn how the shopkeeper should respond to those important customer actions. We present a novel technique for learning which customer actions are important by first learning the hidden causal relationship between customer and shopkeeper actions. In an offline evaluation, we show that our proposed technique significantly outperforms state-of-the-art baselines, in both which customer actions are important and how to respond to them. CCS CONCEPTS • Computing methodologies → Learning from demonstrations; • Human-centered computing → HCI theory, concepts and models.

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Section: Related Workmentioning

confidence: 99%

Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data

Nanavati

Doering

Brščić

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

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“…Robots are used not only for elderly care but also in other various fields. For example, robots offer customer services in places such as hotels and restaurants [10,11]. In the robot communication system, it can target a variety of people, such as young people living alone and children whose parents are going out.…”

Section: Literature Reviewmentioning

confidence: 99%

Robot Partner Development Platform for Human-Robot Interaction Based on a User-Centered Design Approach

2020

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This paper presents a robot partner development platform based on smart devices. Humans communicate with others based on the basic motivations of human cooperation and have communicative motives based on social attributes. Understanding and applying these communicative motives become important in the development of socially-embedded robot partners. Therefore, it is becoming more important to develop robots that can be applied according to needs while taking these human communication elements into consideration. The role of a robot partner is more important in not only on the industrial sector but also in households. However, it seems that it will take time to disseminate robots. In the field of service robots, the development of robots according to various needs is important and the system integration of hardware and software becomes crucial. Therefore, in this paper, we propose a robot partner development platform for human-robot interaction. Firstly, we propose a modularized architecture of robot partners using a smart device to realize a flexible update based on the re-usability of hardware and software modules. In addition, we show examples of implementing a robot system using the proposed architecture. Next, we focus on the development of various robots using the modular robot partner system. Finally, we discuss the effectiveness of the proposed robot partner system through social implementation and experiments.

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Simultaneous Dialog Robot System

Cited by 2 publications

References 1 publication

Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data

Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data

Robot Partner Development Platform for Human-Robot Interaction Based on a User-Centered Design Approach

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