论文标题
通过幻觉场景表示，学习对象安排关系说明
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
论文作者
论文摘要
机器人与人类在环境中并存，并为他们提供服务，需要与他们互动的能力。对此类机器人的一个特殊要求是，他们能够理解空间关系，并可以根据用户表达的空间关系放置对象。在这项工作中，我们提出了一个卷积神经网络，用于从单个输入图像中估算一组空间关系的pixelwise对象放置概率。在培训期间，我们的网络通过将幻觉的高级场景表示形式分类为辅助任务，从而收到学习信号。与以前的方法不同，我们的方法不需要对象的PixelWise关系概率或3D模型，这大大扩大了实际应用中的适用性。我们使用现实世界数据和人类机器人实验获得的结果证明了我们方法在推理放置对象以重现空间关系的最佳方法方面的有效性。可以在https://youtu.be/zazkhtwfmkm上找到我们的实验视频
Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this work, we present a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image. During training, our network receives the learning signal by classifying hallucinated high-level scene representations as an auxiliary task. Unlike previous approaches, our method does not require ground truth data for the pixelwise relational probabilities or 3D models of the objects, which significantly expands the applicability in practical applications. Our results obtained using real-world data and human-robot experiments demonstrate the effectiveness of our method in reasoning about the best way to place objects to reproduce a spatial relation. Videos of our experiments can be found at https://youtu.be/zaZkHTWFMKM
