Automatically-Tuned Model Predictive Control for an Underwater Soft Robot

W. David Null, William Edwards, Dohun Jeong, Teodor Tchalakov, James Menezes, Kris Hauser, Y. Zy

Research output: Contribution to journalArticlepeer-review


Soft robots have desirable qualities for use in underwater environments thanks to their inherent compliance and lack of need for exposed hardware. Nevertheless, these advantages come at the cost of considerable control challenges. Data-driven model predictive control (MPC) is an approach that has shown promise in controlling soft robots. However, manually tuning the many hyperparameters in the learned dynamics model and the optimizer can be extremely tedious. In this work, we explore using data-driven MPC to control an underwater soft robot, and employ the AutoMPC method to automatically tune the hyperparameters and generate the controller. In the process, we extend AutoMPC's capabilities to handle multi-task tuning and we add a barrier cost function to enforce actuator constraints. Our experiments show that the AutoMPC controller reaches targets with significantly higher accuracy and reliability than state-of-the-art baselines both in- and out-of-distribution of the training data.

Original languageEnglish (US)
Pages (from-to)571-578
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number1
StatePublished - Jan 1 2024
Externally publishedYes


  • Machine learning for robot control
  • modeling, control, and learning for soft 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


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