Incremental Robot Learning

Incremental Robot Learning is a machine learning paradigm within human-robot interaction that enables a robot to continuously acquire and refine knowledge from sequential, often real-time, interactions with humans or environments, distinguishing itself by its ability to adapt autonomously without retraining from scratch.

151

Publications

8.4K

Citations

403

Authors

198

Institutions

Publications per year

2017–2026

55

Authors

403

  1. MA
    Minoru Asada

    The University of Osaka, Japan

    4 publications · 389 citations

    4

    h-index

  2. DF
    David Filliat

    École Nationale Supérieure de Techniques Avancées, France

    4 publications · 295 citations

    4

    h-index

  3. HG
    Hatice Güneş

    University of Cambridge, United Kingdom

    3 publications · 99 citations

    3

    h-index

  4. YT
    Yasutake Takahashi

    Tohoku University, Japan

    3 publications · 112 citations

    3

    h-index

  5. BN
    Bojan Nemec

    Jožef Stefan Institute, Slovenia

    3 publications · 56 citations

    3

    h-index

1–5 of 403

Venues

  1. Neurocomputing

    6 publications · 194 citations

    6

    h-index

  2. 5

    h-index

  3. 4

    h-index

  4. Robotics and Autonomous Systems

    4 publications · 145 citations

    4

    h-index

  5. Frontiers in Neurorobotics

    3 publications · 72 citations

    4

    h-index

1–5