Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion

Yu-Ming Chen, Jianshu Hu, and Michael Posa

Reduced-order models (ROM) are popular in online motion planning due to their simplicity. A good ROM for control captures critical task-relevant aspects of the full dynamics while remaining low dimensional. However, planning within the reduced-order space unavoidably constrains the full model, and hence we sacrifice the full potential of the robot. In the community of legged locomotion, this has lead to a search for better model extensions, but many of these extensions require human intuition, and there has not existed a principled way of evaluating the model performance and discovering new models. In this work, we propose a model optimization algorithm that automatically synthesizes reduced-order models, optimal with respect to a user-specified distribution of tasks and corresponding cost functions. To demonstrate our work, we optimized models for a bipedal robot Cassie. We show in simulation that the optimal ROM reduces the cost of Cassie’s joint torques by up to 23% and increases its walking speed by up to 54%. We also show hardware result that the real robot walks on flat ground with 10% lower torque cost. All videos and code can be found at https://sites.google.com/view/ymchen/research/optimal-rom.

@article{Chen2023b,
  title = {Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion},
  author = {Chen, Yu-Ming and Hu, Jianshu and Posa, Michael},
  year = {2024},
  journal = {IEEE Transactions on Robotics (TRO)},
  arxiv = {2301.02075},
  youtube = {NXtue18TsvE},
  doi = {10.1109/TRO.2024.3386390},
  volume = {40},
  number = {},
  pages = {2582-2601},
  url = {https://ieeexplore.ieee.org/document/10494916},
  website = {https://sites.google.com/view/ymchen/research/optimal-rom}
}