论文标题
通过学习与它们互动来学习对象
Learning About Objects by Learning to Interact with Them
论文作者
论文摘要
计算机视觉中的许多显着进步都集中在完全监督的学习机制上，这些学习机制依靠高度策划的数据集进行各种任务。相比之下，人类经常在几乎没有外部监督的情况下了解自己的世界。通过游戏和互动从婴儿那里学习灵感，我们提出了一个计算框架，以发现对象并沿着这种从互动中学习的范式学习其物理特性。我们的代理人将其放置在近乎照相和物理启用的AI2环境中时，它会与世界互动，并在没有任何外部指导的情况下学习对象，几何范围和相对质量。我们的实验表明，该药物可以有效地学习。不仅是为了与以前与之互动的对象，而且还用于来自见证类别的新颖实例以及新颖的对象类别。
Much of the remarkable progress in computer vision has been focused around fully supervised learning mechanisms relying on highly curated datasets for a variety of tasks. In contrast, humans often learn about their world with little to no external supervision. Taking inspiration from infants learning from their environment through play and interaction, we present a computational framework to discover objects and learn their physical properties along this paradigm of Learning from Interaction. Our agent, when placed within the near photo-realistic and physics-enabled AI2-THOR environment, interacts with its world and learns about objects, their geometric extents and relative masses, without any external guidance. Our experiments reveal that this agent learns efficiently and effectively; not just for objects it has interacted with before, but also for novel instances from seen categories as well as novel object categories.
