论文标题
增强连接探索：与数据稀缺的视觉动作计划范例
Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data Scarcity
论文作者
论文摘要
在无法明确计算系统状态（例如操纵可变形物体）的应用程序中，视觉动作计划特别出色，因为它可以直接从原始图像中进行计划。即使深度学习技术已经显着加速了该领域，但其成功的关键要求是大量数据的可用性。在这项工作中，我们建议在数据稀缺的情况下进行增强连接探索（ACE）范式来实现视觉行动计划。 我们基于潜在的空间路线图（LSR）框架，该框架通过在低维潜在空间中建造的图表执行计划。特别是，ACE用于i）通过自主创建新的数据点来增强可用培训数据集，ii）以潜在图中的状态在状态的表示之间创建新的未观察到的连接，iii）以目标方式探索潜在空间的新区域。我们在模拟框堆叠和现实世界折叠任务上验证了所提出的方法，分别显示了刚性和可变形的对象操纵任务的适用性。
Visual action planning particularly excels in applications where the state of the system cannot be computed explicitly, such as manipulation of deformable objects, as it enables planning directly from raw images. Even though the field has been significantly accelerated by deep learning techniques, a crucial requirement for their success is the availability of a large amount of data. In this work, we propose the Augment-Connect-Explore (ACE) paradigm to enable visual action planning in cases of data scarcity. We build upon the Latent Space Roadmap (LSR) framework which performs planning with a graph built in a low dimensional latent space. In particular, ACE is used to i) Augment the available training dataset by autonomously creating new pairs of datapoints, ii) create new unobserved Connections among representations of states in the latent graph, and iii) Explore new regions of the latent space in a targeted manner. We validate the proposed approach on both simulated box stacking and real-world folding task showing the applicability for rigid and deformable object manipulation tasks, respectively.
