混合モデル近似による変分推論：ロボット応用
Variational Inference with Mixture Model Approximation: Robotic Applications
複数目的を満たすロボット姿勢分布を混合モデルで近似する変分推論手法を提案し、ロボティクスの幅広い問題への応用可能性を示した。
著者: Emmanuel Pignat, Teguh Lembono, Sylvain Calinon
分類: cs.RO
原文アブストラクト
We propose a method to approximate the distribution of robot configurations satisfying multiple objectives. Our approach uses variational inference, a popular method in Bayesian computation, which has several advantages over sampling-based techniques. To be able to represent the complex and multimodal distribution of configurations, we propose to use a mixture model as approximate distribution, an approach that has gained popularity recently. In this work, we show the interesting properties of this approach and how it can be applied to a wide range of problems in robotics.
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