论文标题
通过指向机器人新颖的对象
Teaching Robots Novel Objects by Pointing at Them
论文作者
论文摘要
必须在新颖环境中运行并与人合作的机器人必须能够在操作过程中从人类专家那里获取新知识。我们建议通过将一只手指向新的对象，以教授机器人的小说对象。端到端神经网络用于关注指向手指示的新颖对象，然后在新场景中定位对象。为了注意指向手指示的新颖对象，我们提出了一种空间注意调制机制，该机制学会着专注于突出显示的对象，同时忽略场景中的其他对象。我们表明，机器人手臂可以通过将手指向它们来操纵新颖的物体。我们还评估了在使用表情符号和共同对象的真实数据集构建的合成数据集上提出的体系结构的性能。
Robots that must operate in novel environments and collaborate with humans must be capable of acquiring new knowledge from human experts during operation. We propose teaching a robot novel objects it has not encountered before by pointing a hand at the new object of interest. An end-to-end neural network is used to attend to the novel object of interest indicated by the pointing hand and then to localize the object in new scenes. In order to attend to the novel object indicated by the pointing hand, we propose a spatial attention modulation mechanism that learns to focus on the highlighted object while ignoring the other objects in the scene. We show that a robot arm can manipulate novel objects that are highlighted by pointing a hand at them. We also evaluate the performance of the proposed architecture on a synthetic dataset constructed using emojis and on a real-world dataset of common objects.
