布の平滑化のための可視連結ダイナミクス学習
Learning Visible Connectivity Dynamics for Cloth Smoothing
部分点群から布の可視連結グラフを推定し、粒子ベースのダイナミクスモデルを学習することで、シミュレーションで従来手法を大きく上回り、実機へのゼロショット転移で布の平滑化を実現した。
著者: Xingyu Lin, Yufei Wang, Zixuan Huang, David Held
分類: cs.RO, cs.LG
原文アブストラクト
Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions. In contrast to previous model-based approaches that learn a pixel-based dynamics model or a compressed latent vector dynamics, we propose to learn a particle-based dynamics model from a partial point cloud observation. To overcome the challenges of partial observability, we infer which visible points are connected on the underlying cloth mesh. We then learn a dynamics model over this visible connectivity graph. Compared to previous learning-based approaches, our model poses strong inductive bias with its particle based representation for learning the underlying cloth physics; it is invariant to visual features; and the predictions can be more easily visualized. We show that our method greatly outperforms previous state-of-the-art model-based and model-free reinforcement learning methods in simulation. Furthermore, we demonstrate zero-shot sim-to-real transfer where we deploy the model trained in simulation on a Franka arm and show that the model can successfully smooth different types of cloth from crumpled configurations. Videos can be found on our project website.
