Multi-task Imitation Learning

Multi-Task Imitation Learning is a framework within imitation learning that enables a single policy to acquire multiple distinct tasks from expert demonstrations by jointly learning shared representations and task-specific behaviors.

15

Publications

610

Citations

56

Authors

26

Institutions

Publications per year

2017–2026

13

Authors

56

  1. JB
    Jeannette Bohg

    Stanford University, United States

    2 publications · 134 citations

    2

    h-index

  2. DX
    Danfei Xu

    Stanford University, United States

    2 publications · 103 citations

    2

    h-index

  3. LF
    Li Fei-Fei

    Stanford University, United States

    2 publications · 103 citations

    2

    h-index

  4. LS
    Lin Shao

    Stanford University, United States

    2 publications · 134 citations

    2

    h-index

  5. TM
    Toki Migimatsu

    Stanford University, United States

    2 publications · 134 citations

    2

    h-index

1–5 of 56

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 32 citations

    2

    h-index

  2. 1

    h-index

  3. IEEE Transactions on Intelligent Vehicles

    ITIVBL · 1 publications · 23 citations

    1

    h-index

  4. Journal of Cognition and Development

    1 publications · 16 citations

    1

    h-index

  5. IEEE Access

    1 publications · 31 citations

    1

    h-index

1–5