Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks
Kuan Fang,Toki Migimatsu,Ajay Mandlekar,Li Fei-Fei,Jeannette Bohg,Kuan Fang,Toki Migimatsu,Ajay Mandlekar,Li Fei-Fei,Jeannette Bohg
Solving real-world manipulation tasks requires robots to be equipped with a repertoire of skills that can be applied to diverse scenarios. While learning-based methods can enable robots to acquire skills from interaction data, their success relies on collecting training data that covers the diverse range of tasks that the robot may encounter during the test time. However, creating diverse and feas...
