RIKEN Information R&D and Strategy Headquarters Behavior Learning Research Team

Team Director: Yutaka Nakamura (D.Eng.)

Japanese Page

Research Summary

Recently, devices like smart speakers, which understand human language, have become more common. But when people interact, they use more than just words – they also rely on gestures, eye contact, and other nonverbal cues. Our team is researching how these different forms of “interaction” work together so that we can build technology that feels more natural and engaging.

For example, smooth conversations depend on timing and rhythm. We’re developing methods to analyze these “timing shifts” in dialogue to better understand what makes a conversation feel natural and connected. Using what we learn, we’re also creating models that can teach a computer-operated avatar to communicate in more lifelike ways.

Some specific areas we’re working on include:

Interaction Feature Analysis: Understanding what makes interactions smooth and natural.
Conversation Generation Models: Developing technology to generate movements for CG avatars so they can engage in realistic conversations.

Main Research Fields

Complex Systems

Related Research Fields

Engineering
Informatics
Interdisciplinary Science & Engineering
Intelligent robotics
Intelligent informatics
Soft computing

Keywords

Human robot interaction
Generative model
Reinforcement learning
Motion generation
Communication robot

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

1. Yuya Okadome and Yutaka Nakamura.
"Feature extraction method using lag operation for sub-grouped multidimensional time series data."
IEEE Access, Volume: 12, Page(s): 98945 – 98959(2024)
2. Huthaifa Ahmad, Yutaka Nakamura.
"A Study on Designing a Robot with Body Features Tailored for Coexistence with Humans in Daily Life Environments."
33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)(2024)
3. Zhichao Chen, Yutaka Nakamura, Hiroshi Ishiguro.
"Outperformance of Mall-Receptionist Android as Inverse Reinforcement Learning Is Transitioned to Reinforcement Learning,"
IEEE Robotics and Automation Letters(2023)
4. Zhichao Chen, Nakamura Yutaka, Hiroshi Ishiguro.
"Android As a Receptionist in a Shopping Mall Using Inverse Reinforcement Learning"
IEEE/RSJ International Conference on Intelligent Robots and Systems (2022)
5. Huthaifa Ahmad, Yutaka Nakamura
"A robot that is always ready for safe physical interactions"
Interdisciplinary Conference on Mechanics, Computers and Electrics (ICMECE 2022)
6. *Yusuke Nishimura, Yutaka Nakamura, and Hiroshi Ishiguro.
"Human interaction behavior modeling using Generative Adversarial Networks"
Neural Networks, 132, pp.521—531 (2020)
7. *Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, and Hiroshi Ishiguro.
"Intrinsically motivated reinforcement learning for human–robot interaction in the real-world"
Neural Networks, 107, pp. 23—33 (2018)
8. *Yuya Okadome, Yutaka Nakamura, and Hiroshi Ishiguro.
"A confidence-based roadmap using Gaussian process regressio"
Autonomous Robots, 41(4) (2017)
9. *Yutaka Nakamura, Takeshi Mori, Masa-aki Sato, and Shin Ishii.
"Reinforcement learning for a biped robot based on a CPG-actor-critic method"
Neural Networks, 20(6), pp.723-735 (2007)

Lab Members

Principal investigator

Yutaka Nakamura: Team Director

Core members

Huthaifa Abedallah Mohammad Ahmad: Research Scientist
Liliana Villamar Gomez: Postdoctoral Researcher

Contact Information

3rd Floor, Advanced Telecommunications Research Institute International
2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto
619-0288 Japan (Kansai Science City)
Tel: +81-(0)774-95-1360
Email: yutaka.nakamura＠riken.jp