Locomotion Skill Learning

Locomotion Skill Learning is a computational framework within robotics and artificial intelligence that enables autonomous agents to acquire and refine complex movement behaviors through interaction with environments, distinguished from generic motor learning by its focus on adaptive, context-sensitive, and often hierarchical control policies for effective locomotion across diverse terrains and dynamic conditions.

61

Publications

6.5K

Citations

218

Authors

67

Institutions

Publications per year

2017–2026

36

Authors

218

  1. MV
    Michiel van de Panne

    University of British Columbia, Canada

    8 publications · 1.2K citations

    8

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    8 publications · 338 citations

    8

    h-index

  3. XB
    Xue Bin Peng

    University of California, Berkeley, United States

    7 publications · 1.1K citations

    7

    h-index

  4. SH
    Sehoon Ha

    Georgia Institute of Technology, United States

    7 publications · 343 citations

    7

    h-index

  5. JT
    Jie Tan

    Hunan University, China

    5 publications · 171 citations

    5

    h-index

1–5 of 218

Venues

  1. ACM Transactions on Graphics

    TOG · 7 publications · 1.2K citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 7 publications · 240 citations

    7

    h-index

  3. IEEE Robotics and Automation Letters

    4 publications · 237 citations

    4

    h-index

  4. Science Robotics

    3 publications · 1.8K citations

    3

    h-index

  5. Gait & Posture

    2 publications · 103 citations

    2

    h-index

1–5