Sparse summarization of robotic grasping data
Martin Hjelm, Carl Henrik Ek, Renaud J. Detry, Hedvig Kjellström, Danica Kragić · 2013
We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.