SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
Jun Lv,Qiaojun Yu,Lin Shao,Wenhai Liu,Wenqiang Xu,Cewu Lu,Jun Lv,Qiaojun Yu,Lin Shao,Wenhai Liu,Wenqiang Xu,Cewu Lu
Building general-purpose robots to perform a diverse range of tasks in a large variety of environments in the physical world at the human level is extremely challenging. According to [1], it requires the robot learning to be sample-efficient, generalizable, compositional, and incremental. In this work, we introduce a systematic learning framework called SAGCI-system towards achieving these above f...
