概要
Controlling the dynamics of complex systems requires identifying and tuning more and more parameters, leading to increasing adaptation times for new tasks or new robots. In this project, we plan to make this process automatic and online to constantly improve the performance during the robot operation. Classical control techniques, together with statistical tools and machine learning will be exploited to this end.
実施期間
2021-2023資金提供
AIST ITH発表論文
| タイトル | 著者 | 学会/論文誌 | 年 | bib | |
| Learning Bipedal Walking for Humanoids with Current Feedback | R. Singh, Z. Xie, P. Gergondet, F. Kanehiro | IEEE Access | 2023 | ||
| Mc-Mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo | R. Singh, P. Gergondet, F. Kanehiro | IEEE/SICE International Symposium on System Integration | 2023 | ||
| Learning Bipedal Walking on Planned Footsteps for Humanoid Robots | R. Singh, M. Benallegue, M. Morisawa, R. Cisneros-Limón, F. Kanehiro | IEEE-RAS International Conference on Humanoid Robots | 2022 |