モデルベースガウス過程による模倣学習
Learning from demonstration with model-based Gaussian process
人間の実演から得たばらつきをガウス混合回帰ベースの多出力ガウス過程の共分散に組み込み、ロボットが経由点を正確に追従しつつ変動の大きい領域では柔軟に動く枠組みを提案した論文。
著者: Noémie Jaquier, David Ginsbourger, Sylvain Calinon
分類: cs.RO, cs.LG
原文アブストラクト
In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task. In this paper, we propose a novel multi-output Gaussian process (MOGP) based on Gaussian mixture regression (GMR). The proposed approach encapsulates the variability retrieved from the demonstrations in the covariance of the MOGP. Leveraging the generative nature of GP models, our approach can efficiently modulate trajectories towards new start-, via- or end-points defined by the task. Our framework allows the robot to precisely track via-points while being compliant in regions of high variability. We illustrate the proposed approach in simulated examples and validate it in a real-robot experiment.
