A Robustness Analysis of Inverse Optimal Control of Bipedal Walking

John R. Rebula, Stefan Schaal, James Finley, Ludovic Righetti

Research output: Contribution to journalArticlepeer-review


Cost functions have the potential to provide compact and understandable generalizations of motion. The goal of inverse optimal control (IOC) is to analyze an observed behavior which is assumed to be optimal with respect to an unknown cost function, and infer this cost function. Here we develop a method for characterizing cost functions of legged locomotion, with the goal of representing complex humanoid behavior with simple models. To test this methodology we simulate walking gaits of a simple 5 link planar walking model which optimize known cost functions, and assess the ability of our IOC method to recover them. In particular, the IOC method uses an iterative trajectory optimization process to infer cost function weightings consistent with those used to generate a single demonstrated optimal trial. We also explore sensitivity of the IOC to sensor noise in the observed trajectory, imperfect knowledge of the model or task, as well as uncertainty in the components of the cost function used. With appropriate modeling, these methods may help infer cost functions from human data, yielding a compact and generalizable representation of human-like motion for use in humanoid robot controllers, as well as providing a new tool for experimentally exploring human preferences.

Original languageEnglish (US)
Article number8790816
Pages (from-to)4531-4538
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2019


  • Humanoid robots
  • optimization and optimal control
  • underactuated 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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