• Skill-based learning extends the capability of robot learning to tasks with a much longer horizon via informed exploration and temporally abstracted policy learning. [SkiMo 2022, SPiRL 2020]
  • Learning skills and world models from unsupervised interactions or diverse unlabeled data can lead to fast learning of a new task. [TLDR 2024, DreamSmooth 2024]
  • Robot learning in the real world requires high sample efficiency, offline training, and safe exploration. This can be accelerated with the extensive use of large task-agnostic data and simulators. [LEQ 2024, DROID 2024, Open X-Embodiment 2024]


We are now on track to fund our research :)

  • image for NRF Outstanding Young Scientist

    Learning General-Purpose Humanoid Robots via Robot Abstraction and Active 3D Perception

    NRF Outstanding Young Scientist

    $200k for 5 years

    Apr 2024 - Mar 2029

  • image for ETRI

    Generalizable Robotic Manipulation



    Apr 2024 - Oct 2024

  • image for LG Electronics

    Skill-based Reinforcement Learning for Mobile Bimanual Manipulation

    LG Electronics


    Mar 2024 - Dec 2024

  • image for Yonsei University / IITP

    AI Graduate School Program

    Yonsei University / IITP

    $20K for 6 years

    Mar 2024 - Feb 2030

  • image for Yonsei University Future-Leading Research Initiative

    Startup Funding

    Yonsei University Future-Leading Research Initiative

    $40K for 3 years

    May 2024 - Apr 2027