Learning Visible Connectivity Dynamics for Cloth Smoothing
Learning Visible Connectivity Dynamics for Cloth Smoothing
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发表时间：
2021-05
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通讯作者：
Xingyu Lin;Yufei Wang;David Held
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作者：
Xingyu Lin;Yufei Wang;David Held
由于布料的复杂动力学，缺乏低维状态表示和自遮挡，机器人对布料的操纵仍然是机器人技术的挑战。与之前基于模型的方法学习基于像素的动力学模型或压缩潜在矢量动力学相比，我们建议从部分点云观测中学习基于粒子的动力学模型。为了克服部分可观测性的挑战，我们推断哪些可见点是连接在底层布网格上的。然后，我们在这个可见的连通性图上学习一个动态模型。与以前的基于学习的方法相比，我们的模型具有很强的归纳偏差，其基于粒子的表示用于学习潜在的布料物理；它对视觉特征是不变的；而且预测可以更容易地可视化。我们表明我们的方法在仿真中大大优于以前最先进的基于模型和无模型的强化学习方法。此外，我们演示了零射击模拟到真实的转移，我们将模拟训练的模型部署在Franka手臂上，并显示该模型可以成功地从皱褶配置平滑不同类型的布。视频可以在我们的项目网站上找到。
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.