LIBERO is a lifelong robot learning benchmark for studying knowledge transfer.

In the future, robots will be pretrained with a diverse set of skills in factory before deployment. However, we believe it is also a must for them to learn with their human users throughout their lifetime to really become a personalized embodied agent. This pertains to the paradigm of lifelong learning in decision making (LLDM). While LLDM is an important research question, currently the community lacks good testbeds for studying LLDM. To this end, we propose LIBERO.

The full release is coming soon!


  • Procedural generation pipeline from human activity datasets
  • 130 tasks
  • 65,000 high-quality demonstrations for sample-efficient leanring
  • Build on top of a modularized robot simulator, Robosuite
  • Multiple implentation of neural architectures, lifelong learning algorithms.
  • The LIBERO Team

    Bo Liu portrait Bo Liu
    Yifeng Zhu portrait Yifeng Zhu
    Chongkai Gao portrait Chongkai Gao
    Yihao Feng portrait Yihao Feng
    Qiang Liu portrait Qiang Liu
    Yuke Zhu portrait Yuke Zhu
    Peter Stone portrait Peter Stone