2020
Data-Efficient and Safe Learning for Humanoid Locomotion Aided by a Dynamic Balancing Model
RA-L 2020
In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relied on a low-dimensional robot model, commonly used in high-level Walking Pattern Gen