Model-Based Generalization under Parameter Uncertainty Using Path Integral Control

Ian Abraham*, Ankur Handa, Nathan Ratliff, Kendall Lowrey, Todd D. Murphey, Dieter Fox

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


This letter addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy formulation of path integral control, we derive a natural extension to the path integral control that embeds uncertainty into action and provides robustness for model-based robot planning. Our algorithm is applied to a diverse set of tasks using different robots and validate our results in simulation and real-world experiments. We further show that our method is capable of running in real-time without loss of performance. Videos of the experiments as well as additional implementation details can be found at

Original languageEnglish (US)
Article number8988215
Pages (from-to)2864-2871
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2020


  • Model learning for control
  • learning and adaptive systems

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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