Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li,Nathan Lambert,Roberto Calandra,Franziska Meier,Akshara Rai,Tianyu Li,Nathan Lambert,Roberto Calandra,Franziska Meier,Akshara Rai
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical framework that improves sample-efficiency and generalizability of learned locomotion skills on real-world robots. Our approach divides the problem of goal-oriented locomotion into two sub-problems: learning diverse primitives skills, and using model...
