単一軌道から変形可能な線状物体のダイナミクスを学習
Learning deformable linear object dynamics from a single trajectory
変形可能な線状物体を剛体の連鎖と弾性関節でモデル化し、物理情報を組み込んだニューラルODEを30秒のデータだけで学習して高精度な運動予測を実現した。
著者: Shamil Mamedov, A. René Geist, Ruan Viljoen, Sebastian Trimpe, Jan Swevers
分類: cs.RO
原文アブストラクト
The manipulation of deformable linear objects (DLOs) via model-based control requires an accurate and computationally efficient dynamics model. Yet, data-driven DLO dynamics models require large training data sets while their predictions often do not generalize, whereas physics-based models rely on good approximations of physical phenomena and often lack accuracy. To address these challenges, we propose a physics-informed neural ODE capable of predicting agile movements with significantly less data and hyper-parameter tuning. In particular, we model DLOs as serial chains of rigid bodies interconnected by passive elastic joints in which interaction forces are predicted by neural networks. The proposed model accurately predicts the motion of an robotically-actuated aluminium rod and an elastic foam cylinder after being trained on only thirty seconds of data. The project code and data are available at: \url{https://tinyurl.com/neuralprba}
