Interactive Imitation Learning

Interactive Imitation Learning is a method within the field of imitation learning and learning from demonstration that enables an agent to acquire control policies through real-time, bidirectional interactions with an expert, thereby distinguishing itself from passive imitation approaches by incorporating dynamic feedback and iterative refinement during the learning process.

23

Publications

828

Citations

88

Authors

39

Institutions

Publications per year

2017–2026

15

Authors

88

  1. JK
    Jens Kober

    Delft University of Technology, Netherlands

    4 publications · 105 citations

    4

    h-index

  2. MG
    Michael Gienger

    Honda (Germany), Germany

    2 publications · 82 citations

    2

    h-index

  3. CC
    Carlos Celemin

    Delft University of Technology, Netherlands

    2 publications · 67 citations

    2

    h-index

  4. EN
    Ellen Novoseller

    University of California, Berkeley, United States

    2 publications · 48 citations

    2

    h-index

  5. MM
    Manuel Mühlig

    Honda (Germany), Germany

    2 publications · 82 citations

    2

    h-index

1–5 of 88

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 167 citations

    4

    h-index

  2. Advances in Space Research

    1 publications · 13 citations

    1

    h-index

  3. Pattern Recognition Letters

    1 publications · 10 citations

    1

    h-index

  4. 1

    h-index

  5. Ocean Engineering

    1 publications · 20 citations

    1

    h-index

1–5