Publication in fiscal year 2021 (Apr. 2021 – Mar. 2022)
- 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)
- K. Sugimoto, T. Aihara, M. Ogura and K. Hanada,
“Gain scheduling for sampled-data state estimation over lossy networks,” Transactions of ISCIE, to appear
- 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)
- 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)
- K. Sugimoto, and W. Imahayashi,
“Establishment of Strictly Positive Real Condition for Tuning MIMO Feedforward Control,” to appear in IEEE Control Systems Letters, Vol. 6, pp. 1454-1459 (2022)
- 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).
- 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).
- 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).