Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

@article{Tucker2020HumanPL,
  title={Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits},
  author={M. Tucker and Myra Cheng and Ellen R. Novoseller and Richard Cheng and Yisong Yue and J. Burdick and A. Ames},
  journal={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  pages={3423-3430}
}
  • M. Tucker, Myra Cheng, +4 authors A. Ames
  • Published 2020
  • Computer Science
  • 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional… Expand
4 Citations

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