This paper presents a method for learning the parameters of rhythmic walking to generate purposive humanoid motions. The controller consists of the two layers: rhythmic walking is...
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
An important goal for the generative and developmental systems (GDS) community is to show that GDS approaches can compete with more mainstream approaches in machine learning (ML)....
Planning for real robots to act in dynamic and uncertain environments is a challenging problem. A complete model of the world is not viable and an integration of deliberation and ...
Manuela M. Veloso, Elly Winner, Scott Lenser, Jame...
We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then tra...
Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richa...