Sparse Summarization of Robotic Grasp Data
نویسندگان
چکیده
In this paper we propose a new approach for learning a summarized representation of high dimensional continuous data. We apply the model to learn efficient representations of grasp data for two robotic scenarios that facilitates a compact summarization. Our technique consists of a Bayesian non-parametric model capable of encoding highdimensional data from complex distributions using a sparse summarization. In specific the method marries techniques from probabilistic dimensionality reduction and clustering to reach a solution. We show how the summarization provided by the model significantly benefits interpretations of data for a robotics grasping task.
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