擬似剛体ネットワーク：部分観測から解釈可能な変形物体ダイナミクスを学習
Pseudo-rigid body networks: learning interpretable deformable object dynamics from partial observations
変形する線状物体（DLO）を擬似剛体（PRB）モデルに基づく剛体リンクとして表現し、物理情報を組み込んだエンコーダと動力学ネットワークを組み合わせて、部分観測から解釈可能かつ高精度なダイナミクス予測を実現した。
著者: Shamil Mamedov, A. René Geist, Jan Swevers, Sebastian Trimpe
分類: cs.RO, cs.LG
原文アブストラクト
Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficient. In this work, we draw inspiration from the pseudo-rigid body method (PRB) and model a DLO as a serial chain of rigid bodies whose internal state is unrolled through time by a dynamics network. This dynamics network is trained jointly with a physics-informed encoder that maps observed motion variables to the DLO's hidden state. To encourage the state to acquire a physically meaningful representation, we leverage the forward kinematics of the PRB model as a decoder. We demonstrate in robot experiments that the proposed DLO dynamics model provides physically interpretable predictions from partial observations while being on par with black-box models regarding prediction accuracy. The project code is available at: http://tinyurl.com/prb-networks
