Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots

    •  Schperberg, A., Di Cairano, S., Menner, M., "Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots", IEEE Robotics and Automation Letters, DOI: 10.1109/​LRA.2022.3185387, Vol. 7, No. 3, pp. 7802-7809, June 2022.
      BibTeX TR2022-085 PDF
      • @article{Schperberg2022jun,
      • author = {Schperberg, Alexander and Di Cairano, Stefano and Menner, Marcel},
      • title = {Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots},
      • journal = {IEEE Robotics and Automation Letters},
      • year = 2022,
      • volume = 7,
      • number = 3,
      • pages = {7802--7809},
      • month = jun,
      • doi = {10.1109/LRA.2022.3185387},
      • url = {}
      • }
  • MERL Contact:
  • Research Areas:

    Control, Dynamical Systems, Machine Learning, Optimization, Robotics


This letter presents an approach for auto-tuning feedback controllers and online trajectory planners to achieve robust locomotion of a legged robot. The auto-tuning approach uses an Unscented Kalman Filter (UKF) formulation, which adapts/calibrates control parameters online using a recursive implementation. In particular, this letter shows how to use the auto-tuning approach to calibrate cost function weights of a Model Predictive Control (MPC) stance controller and feedback gains of a swing controller for a quadruped robot. Furthermore, this letter extends the auto-tuning approach to calibrating parameters of an online trajectory planner, where the height of a swing leg and the robot’s walking speed are optimized, while minimizing its energy consumption and foot slippage. This allows us to generate stable reference trajectories online and in real time. Results using a high-fidelity Unitree A1 robot simulator in Gazebo provided by the robot manufacturer show the advantages of using auto-tuning for calibrating feedback controllers and for computing reference trajectories online for reduced development time and improved tracking performance