Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
复制标题
基于图的可变形和刚性物体之间相互作用的特定任务预测模型
DOI：
10.1109/iros51168.2021.9636660
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
D. Kragic
中科院分区：
文献类型：
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作者：
Zehang Weng;Fabian Paus;Anastasiia Varava;Hang Yin;T. Asfour;D. Kragic
捕捉场景动态和预测未来的场景状态是具有挑战性的，但必不可少的机器人操作任务，特别是当场景包含刚性和可变形的对象。在这项工作中，我们贡献了一个模拟环境，并生成一个新的数据集的任务特定的操作，涉及刚性物体和可变形袋之间的相互作用。该数据集包含了丰富的场景，包括不同的对象大小，对象数量和操作动作。我们提出了一个以对象为中心的图形表示和两个模块，这是主动预测模块（APM）和位置预测模块（PPM）的基础上的图形神经网络的编码-处理-解码架构的动态学习。在推理阶段，我们建立了一个两阶段模型的基础上学习的单时间步预测模块。我们将具有不同预测范围的联合收割机模块组合成一个解决长期预测的混合范围模型。在消融研究中，我们展示了两阶段模型用于单时间步预测的好处以及混合视野模型用于长期预测任务的有效性。补充材料可在https://github.com/wengzehang/deformable_rigid_interaction_prediction上获得
Capturing scene dynamics and predicting the future scene state is challenging but essential for robotic manipulation tasks, especially when the scene contains both rigid and deformable objects. In this work, we contribute a simulation environment and generate a novel dataset for task-specific manipulation, involving interactions between rigid objects and a deformable bag. The dataset incorporates a rich variety of scenarios including different object sizes, object numbers and manipulation actions. We approach dynamics learning by proposing an object-centric graph representation and two modules which are Active Prediction Module (APM) and Position Prediction Module (PPM) based on graph neural networks with an encode-process-decode architecture. At the inference stage, we build a two-stage model based on the learned modules for single time step prediction. We combine modules with different prediction horizons into a mixed-horizon model which addresses long-term prediction. In an ablation study, we show the benefits of the two-stage model for single time step prediction and the effectiveness of the mixed-horizon model for long-term prediction tasks. Supplementary material is available at https://github.com/wengzehang/deformable_rigid_interaction_prediction
DOI：
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发表时间：
2020-11
期刊：
ArXiv
影响因子：
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作者：
Xingyu Lin;Yufei Wang;Jake Olkin;David Held
通讯作者：
Xingyu Lin;Yufei Wang;Jake Olkin;David Held