RIKEN Center for Advanced Intelligence Project Robot Learning Team
Team Director: Takayuki Osa (Ph.D.)
Research Summary

To realize autonomous robotic systems that function effectively in the real world, it is essential to develop frameworks that enable efficient task learning and rapid adaptation to diverse environments.
Our team focuses on developing algorithms and real-world robotic systems that allow robots to efficiently learn behaviors and operate autonomously, exploring approaches such as reinforcement learning and imitation learning.
Main Research Fields
- Informatics
Related Research Fields
- Engineering
- Humanities & Social Sciences
- Interdisciplinary Science & Engineering
- Mathematical & Physical Sciences
- Intelligent robotics
- Intelligent informatics
- Robotics and intelligent system
Keywords
- Robot Learning
- Reinforcement Learning
- Imitation Learning
- Motion Planning
Selected Publications
Papers with an asterisk(*) are based on research conducted outside of RIKEN.
- 1.
T. Osa and T. Harada
"Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning"
Proceedings of the International Conference on Machine Learning (ICML), 2024. - 2.
J. Ackermann, T. Osa, and M. Sugiyama
"Offline Reinforcement Learning from Datasets with Structured Non-Stationarity"
Proceedings of the Reinforcement Learning Conference (RLC), 2024. - 3.
M. Omura, T. Osa, Y. Mukuta, T. Harada
"Stabilizing Extreme Q-learning by Maclaurin Expansion"
Proceedings of the Reinforcement Learning Conference (RLC), 2024. - 4.
*T. Osa and T. Harada
"Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behavior and Adversarial Style Sampling for Assistive Tasks"
Proceedings of the IEEE International Conferences on Robotics and Automation (ICRA), 2024. - 5.
N. Morihira, P. Deo, M. Bhadu, A. Hayashi, T. Hasegawa, S. Otsubo, T. Osa
"Touch-Based Manipulation with Multi-Fingered Robot using Off-policy RL and Temporal Contrastive Learning"
Proceedings of the IEEE International Conferences on Robotics and Automation (ICRA), 2024. - 6.
T. Osa
"Motion Planning by Learning the Solution Manifold in Trajectory Optimization"
The International Journal of Robotics Research, Vol. 41, No. 3, pp. 291-311, 2022. - 7.
T. Osa
"Multimodal Trajectory Optimization for Motion Planning"
The International Journal of Robotics Research, Vol. 39 No. 8, pp. 983–-1001, 2020. - 8.
*T. Osa, V. Tangkaratt, M. Sugiyama.
"Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization"
Proceedings of the International Conference on Learning Representations (ICLR), 2019. - 9.
*T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P Abbeel, and J. Peters.
"An Algorithmic Perspective on Imitation Learning"
Trends and Foundations in Robotics, Vol. 7: No. 1-2, pp 1-179, 2018. - 10.
*T. Osa, N. Sugita, and M. Mitsuishi.
"Online Trajectory Planning in Dynamic Environments for Surgical Task Automation"
Proceedings of Robotics: Science and Systems (R:SS), 2014.
Related Links
Lab Members
Principal investigator
- Takayuki Osa
- Team Director
Careers
Position | Deadline |
---|---|
Seeking Research Scientists or Postdoctoral Researchers (W24316) | Open until filled |
Contact Information
Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi,
Chuo-ku, Tokyo
103-0027, Japan
Email: takayuki.osa@riken.jp