We consider apprenticeship learning—learning from expert demonstrations—in the setting of large, complex domains. Past work in apprenticeship learning requires that the expert...
— Legged robots have the potential to navigate a much larger variety of terrain than their wheeled counterparts. In this paper we present a hierarchical control architecture that...
We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to p...
Matthew Zucker, Nathan D. Ratliff, Martin Stolle, ...
This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the ...
— Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning to the proble...