Learning-Based Balance Control of Wheel-Legged Robots

Leilei Cui, Shuai Wang, Jingfan Zhang, Dongsheng Zhang, Jie Lai, Yu Zheng, Zhengyou Zhang, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review


This letter studies the adaptive optimal control problem for a wheel-legged robot in the absence of an accurate dynamic model. A crucial strategy is to exploit recent advances in reinforcement learning (RL) and adaptive dynamic programming (ADP) to derive a learning-based solution to adaptive optimal control. It is shown that suboptimal controllers can be learned directly from input-state data collected along the trajectories of the robot. Rigorous proofs for the convergence of the novel data-driven value iteration (VI) algorithm and the stability of the closed-loop robot system are provided. Experiments are conducted to demonstrate the efficiency of the novel adaptive suboptimal controller derived from the data-driven VI algorithm in balancing the wheel-legged robot to the equilibrium.

Original languageEnglish (US)
Article number9497675
Pages (from-to)7667-7674
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2021


  • Machine learning for robot control
  • optimization and optimal control
  • wheeled robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


Dive into the research topics of 'Learning-Based Balance Control of Wheel-Legged Robots'. Together they form a unique fingerprint.

Cite this