Multimodal child-robot interaction: building social bonds

By Tony Belpaeme, Paul E. Baxter, Robin Read, R. Wood, Heriberto Cuayáhuitl, B. Kiefer, Stefania Racioppa, Ivana Kruijff-Korbayová, Georgios Athanasopoulos, Valentin Enescu and 15 more. J. Hum. Robot Interact., 2013

319 citations recorded by the source corpus.

For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competencies and integrating them to form an autonomous robotic system for evaluation "in the wild." The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.

Subfields: Artificial Intelligence, Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/7c2964512e2a10eb8a91528f2fa4d7148ffc88a3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ognjen Rudovic
2. James Kennedy
3. Paul E. Baxter
4. Björn Schuller
5. Rosalind W. Picard
6. Miles Dai
7. Tony Belpaeme
8. Jaeryoung Lee

In Social Psychology

1. James Kennedy
2. Björn Schuller
3. Rosalind W. Picard
4. Tony Belpaeme
5. Stephen H. Fairclough

In Human-Computer Interaction

1. Björn Schuller
2. Rosalind W. Picard

Citations in the corpus

• Personalized machine learning for robot perception of affect and engagement in autism therapy (2018, background): “, cameras and microphones) capturing different modalities (face, body, and voice) of child’s behaviors (20).”
• The Robot Who Tried Too Hard: Social Behaviour of a Robot Tutor Can Negatively Affect Child Learning (2015, background): “Large projects have now turned their attention towards child learning, for example the ALIZ-E project [3] in Europe, and an NSF funded Socially Assistive Robotics project in the USA [20].”
• Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI) (2021, unknown): “, [26]–[31]), healthcare and therapy (e.”