Project Description
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.
Period
2021-2023Funded by
AIST ITHRelated Publications (including JRL contributions)
| Title | Authors | Conference/Book | Year | bib | |
| 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 for Humanoids with Current Feedback | R. Singh, Z. Xie, P. Gergondet, F. Kanehiro | IEEE Access | 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 |