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Scene Mover: Automatic Move Planning for Scene Arrangement by Deep Reinforcement Learning

机译:场景动感器:深增强学习的场景安排自动移动规划

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We propose a novel approach for automatically generating a move plan forscene arrangement.1 Given a scene like an apartment with many furnitureobjects, to transform its layout into another layout, one would need to determinea collision-free move plan. It could be challenging to design this planmanually because the furniture objects may block the way of each other ifnot moved properly; and there is a large complex search space of move actionsequences that grow exponentially with the number of objects. To tacklethis challenge, we propose a learning-based approach to generate a moveplan automatically. At the core of our approach is a Monte Carlo tree thatencodes possible states of the layout, based on which a search is performed to move a furniture object appropriately in the current layout. We trained apolicy neural network embedded with a LSTM module for estimating thebest actions to take in the expansion step and simulation step of the MonteCarlo tree search process. Leveraging the power of deep reinforcement learning,the network learned how to make such estimations through millions oftrials of moving objects. We demonstrated our approach for moving objectsunder different scenarios and constraints. We also evaluated our approachon synthetic and real-world layouts, comparing its performance with thatof humans and other baseline approaches.
机译:我们提出了一种自动生成移动计划的新方法场景安排.1给出了像许多家具的公寓这样的场景对象,要将其布局转换为另一个布局,需要确定无碰撞移动计划。设计这个计划可能会挑战手动是因为家具物体可能会阻止彼此的方式没有正确移动;并且有一个大型的移动动作搜索空间序列以对象数量呈指数级增长。托架这一挑战,我们提出了一种基于学习的方法来产生举动自动计划。在我们的方法的核心是一个蒙特卡罗树基于该布局的可能状态,基于该布局的可能状态,以执行搜索以在当前布局中适当地移动家具对象。我们训练了一个嵌入LSTM模块的政策神经网络,用于估计采用蒙特扩展步骤和模拟步骤的最佳行动Carlo树搜索过程。利用深增强学习的力量,网络了解如何通过数百万的估计移动物体的试验。我们展示了移动物体的方法在不同的场景和约束下。我们还评估了我们的方法在合成和现实世界布局上,将其表现与其进行比较人类与其他基线方法。

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