Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers
Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers
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DOI：
10.1109/icra48891.2023.10161204
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Yixuan Huang;Adam Conkey;Tucker Hermans
中科院分区：
文献类型：
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作者：
Yixuan Huang;Adam Conkey;Tucker Hermans
在人类环境中，物体很少是孤立存在的。因此，我们希望我们的机器人能够推理多个对象如何相互关联，以及当机器人与世界交互时这些关系可能如何变化。为此，我们提出了一种用于多对象操纵的新型图神经网络框架，以预测对象间关系如何改变给定的机器人动作。我们的模型在部分视点云上运行，可以推理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，我们的模型可以实现多步规划以达到目标目标关系。我们展示了纯粹在模拟中训练的模型可以很好地转移到现实世界。我们的规划器使机器人能够使用推动和拾放技能重新排列不同数量的具有各种形状和尺寸的物体。
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.