Publication in fiscal year 2021 (Apr. 2021 – Mar. 2022)

Journal Paper

  1. T. Kobayashi, E. Dean-Leon, J. R. Guadarrama-Olvera, F. Bergner, and G. Cheng,
    “Whole-Body Multicontact Haptic Human-Humanoid Interaction based on Leader-Follower Switching: A robot dance of the “Box Step”,” Advanced Intelligent Systems, (2021)
  2. K. Sugimoto, T. Aihara, M. Ogura and K. Hanada,
    “Gain scheduling for sampled-data state estimation over lossy networks,” Transactions of the Institute of Systems, Control and Information Engineers, Vol. 34, No. 11, pp. 287-293 (2021)
  3. T. Aotani, T. Kobayashi, and K. Sugimoto,
    “Bottom-up Multi-agent Reinforcement Learning by Reward Shaping for Cooperative-Competitive Tasks,” Applied Intelligence, Vol. 51, No. 7, pp.4434-4452, (2021)
  4. H. Fujiishi, T. Kobayashi, and K. Sugimoto,
    “Safe and Efficient Imitation Learning by Clarification of Experienced Latent Space,” Advanced Robotics, Vol. 35, No. 16, pp.1012-1027, (2021)
  5. K. Sugimoto, and W. Imahayashi,
    “Establishment of Strictly Positive Real Condition for Tuning MIMO Feedforward Control,”  IEEE Control Systems Letters, Vol. 6, pp. 1454-1459,  Early Access (2022)
  6. T. Aotani, T. Kobayashi, and K. Sugimoto,
    “Meta-Optimization of Bias-Variance Trade-off in Stochastic Model Learning,” IEEE Access, Vol. 9, pp. 148783–148799, (2021)
  7. K. Hanada, Y. Amemiya, and K. Sugimoto,
    “A Distributed Asynchronous Heuristic Algorithm in Generalized Mutual Assignment Problem,” Transactions of the Japanese Society for Artificial Intelligence, Vol. 37, No. 2, pp. B-L81_1-11, (2022)
  8. T. Kobayashi,
    “Adaptive and multiple time-scale eligibility traces for online deep reinforcement learning,” Robotics and Autonomous Systems, (accepted for publication)
  9. T. Shimizu, H. Funakoshi, T. Kobayashi, and K. Sugimoto,
    “Reduction of Noise and Vibration in Drum type Washing Machine using Q-learning,” Control Engineering Practice, (accepted for publication)

International Conference

  1. T. Kobayashi,
    “Proximal Policy Optimization with Relative Pearson Divergence,”
    IEEE International Conference on Robotics and Automation, TuBT5, Xi’an, China (with online), 2021.06.01-03 (06.01).
  2. T. Kobayashi,
    “Adaptive Eligibility Traces for Online Deep Reinforcement Learning,”
    International Conference on Intelligent Autonomous Systems, pp.407-418, Singapore (online), 2021.06.23-25 (06.24).
  3. W.E.L. Ilboudo, T. Kobayashi, K. Sugimoto,
    “Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning,”
    IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.7828-7834, Prague, Czech Republic (online), 2021.09.28-30 (09.30).
  4. Y. Amemiya, K. Hanada, and K. Sugimoto, “Adaptive Step Size for a Consensus based Distributed Subgradient Method in Generalized Mutual Assignment Problem,” the 11th IIAI International Congress on Advanced Applied Informatics (online), 2021.12.17-19 (12.19).

Press Release

Invited Talk

Book Chapters