论文标题
使用图神经网络关系分类器进行多对象操作的计划
Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers
论文作者
论文摘要
物体很少在人类环境中孤立地坐着。因此，我们希望我们的机器人推理多个对象如何相互关系，以及这些关系在机器人与世界互动时可能会发生变化。为此，我们提出了一个新型的图形神经网络框架，用于多对象操作，以预测对机器人的关系如何变化的机器人动作。我们的模型在部分视图点云上运行，可以推理在操作过程中动态交互的多个对象。通过在学习的潜在图嵌入空间中学习动态模型，我们的模型使多步规划可以达到目标目标关系。我们显示了纯粹在模拟中训练的模型，可以很好地传输到现实世界。我们的计划器使机器人能够使用推送和挑选和位置技能重新排列可变数量的对象。
Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change as the robot interacts with the world. To this end, we propose a novel graph neural network framework for multi-object manipulation to predict how inter-object relations change given robot actions. Our model operates on partial-view point clouds and can reason about multiple objects dynamically interacting during the manipulation. By learning a dynamics model in a learned latent graph embedding space, our model enables multi-step planning to reach target goal relations. We show our model trained purely in simulation transfers well to the real world. Our planner enables the robot to rearrange a variable number of objects with a range of shapes and sizes using both push and pick and place skills.
