Adaptive Exploration

Adaptive Exploration is a methodological framework within autonomous exploration that dynamically adjusts exploration strategies based on real-time environmental feedback and uncertainty estimates to optimize information gain and task efficiency.

114

Publications

4K

Citations

313

Authors

145

Institutions

Publications per year

2017–2026

63

Authors

313

  1. PK
    Pradeep K. Khosla

    Carnegie Mellon University, United States

    4 publications · 189 citations

    4

    h-index

  2. KH
    Kian Hsiang Low

    National University of Singapore, Singapore

    4 publications · 189 citations

    4

    h-index

  3. JM
    John M. Dolan

    Carnegie Mellon University, United States

    3 publications · 169 citations

    3

    h-index

  4. XL
    Xin Liu

    University of Illinois Urbana-Champaign, United States

    2 publications · 25 citations

    2

    h-index

  5. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    2 publications · 55 citations

    2

    h-index

1–5 of 313

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 163 citations

    8

    h-index

  2. IEEE Access

    3 publications · 94 citations

    3

    h-index

  3. ArXiv.org

    Repository · 2 publications · 40 citations

    2

    h-index

  4. The International Journal of Robotics Research

    IJRR · 2 publications · 117 citations

    2

    h-index

  5. Sensors

    2 publications · 38 citations

    2

    h-index

1–5