Model-based Locomotion Learning

Model-Based Locomotion Learning is a locomotion learning paradigm that belongs to the class of model-based reinforcement learning approaches, distinguished by its reliance on an explicit forward model of the environment dynamics to plan or optimize locomotor policies.

10

Publications

749

Citations

41

Authors

10

Institutions

Publications per year

2017–2026

7

Authors

41

  1. DH
    David Hoeller

    ETH Zurich, Switzerland

    2 publications · 38 citations

    2

    h-index

  2. BB
    Byron Boots

    Georgia Institute of Technology, United States

    2 publications · 38 citations

    2

    h-index

  3. ZX
    Zhaoming Xie

    University of British Columbia, Canada

    2 publications · 38 citations

    2

    h-index

  4. AG
    Animesh Garg

    University of Toronto, Canada

    2 publications · 38 citations

    2

    h-index

  5. AA
    Animashree Anandkumar

    University of California, Irvine, United States

    2 publications · 38 citations

    2

    h-index

1–5 of 41

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 38 citations

    2

    h-index

  2. IEEE Robotics and Automation Letters

    2 publications · 88 citations

    2

    h-index

  3. 1

    h-index

  4. IEEE Robotics & Automation Magazine

    1 publications · 79 citations

    1

    h-index

  5. Physical Review Fluids

    PRFluids · 1 publications · 78 citations

    1

    h-index

1–5