Multi-robot Imitation Learning

Multi-Robot Imitation Learning is a machine learning framework in which a collective of autonomous robots acquires coordinated behaviors by mimicking expert demonstrations, distinguished from single-agent or non-imitative multi-robot approaches through its reliance on observational learning at scale.

4

Publications

525

Citations

11

Authors

8

Institutions

Publications per year

2017–2026

3

Authors

11

  1. YS
    Yunfei Shi

    Carnegie Mellon University, United States

    1 publications · 402 citations

    1

    h-index

  2. ZW
    Zheyuan Wang

    Georgia Institute of Technology, United States

    1 publications · 89 citations

    1

    h-index

  3. MG
    Matthew Gombolay

    Georgia Institute of Technology, United States

    1 publications · 89 citations

    1

    h-index

  4. HC
    Howie Choset

    Carnegie Mellon University, United States

    1 publications · 402 citations

    1

    h-index

  5. GS
    Guillaume Sartoretti

    National University of Singapore, Singapore

    1 publications · 402 citations

    1

    h-index

1–5 of 11

Venues

  1. IEEE Robotics and Automation Letters

    2 publications · 491 citations

    2

    h-index

  2. Advanced Intelligent Systems

    1 publications · 12 citations

  3. Advanced Robotics

    1 publications · 10 citations

1–4