ベイズガウス混合モデルによる制御ポリシーの能動的改善
Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
ベイズガウス混合モデルの認識的不確かさに基づき、閉形式の情報密度コストを最適化して新たなクエリ点を選ぶ能動学習フレームワークを提案し、障害物環境での到達タスクを実機で実証した。
著者: Hakan Girgin, Emmanuel Pignat, Noémie Jaquier, Sylvain Calinon
分類: cs.RO
原文アブストラクト
Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this paper, we introduce a novel active learning framework in order to improve the generalization capabilities of control policies. The proposed approach is based on the epistemic uncertainties of Bayesian Gaussian mixture models (BGMMs). We determine the new query point location by optimizing a closed-form information-density cost based on the quadratic R\'enyi entropy. Furthermore, to better represent uncertain regions and to avoid local optima problem, we propose to approximate the active learning cost with a Gaussian mixture model (GMM). We demonstrate our active learning framework in the context of a reaching task in a cluttered environment with an illustrative toy example and a real experiment with a Panda robot.
