论文标题
使用对比度估计来学习可变形对象的预测性表示
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
论文作者
论文摘要
由于学习可策划的视觉表示和复杂的动态模型的困难，因此使用基于视觉模型的学习进行可变形对象操作是具有挑战性的。在这项工作中，我们提出了一个新的学习框架，该框架可以使用对比度估计共同优化视觉表示模型和动力学模型。使用通过在表上随机扰动可变形对象收集的仿真数据，我们以离线方式学习了这些对象的潜在动力学模型。然后，使用学识渊博的模型，我们使用简单的基于模型的计划来解决具有挑战性的可变形对象操纵任务，例如扩散绳索和布料。在实验上，我们在绳索和布操纵套件上的基于标准模型的学习技术方面的性能大大改善。最后，我们通过纯粹的模拟数据训练的数据转移到通过域随机化的真实PR2机器人的视觉操作策略。
Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models. In this work, we propose a new learning framework that jointly optimizes both the visual representation model and the dynamics model using contrastive estimation. Using simulation data collected by randomly perturbing deformable objects on a table, we learn latent dynamics models for these objects in an offline fashion. Then, using the learned models, we use simple model-based planning to solve challenging deformable object manipulation tasks such as spreading ropes and cloths. Experimentally, we show substantial improvements in performance over standard model-based learning techniques across our rope and cloth manipulation suite. Finally, we transfer our visual manipulation policies trained on data purely collected in simulation to a real PR2 robot through domain randomization.
