论文标题
柏拉图：通过以对象为中心的游戏来预测潜在的能力
PLATO: Predicting Latent Affordances Through Object-Centric Play
论文作者
论文摘要
以可扩展的方式构建各种操纵技巧的曲目仍然是机器人技术中未解决的挑战。解决这一挑战的一种方法是在非结构化的人类游戏中，人类在环境中自由运作以达到未指定的目标。游戏是一种简单且便宜的方法，用于在环境中收集具有广泛状态和目标覆盖的多样化用户演示。由于这种不同的覆盖范围，现有的从游戏中学习的方法对离线数据分布的在线政策偏差更加牢固。但是，这些方法通常很难在场景变化和具有挑战性的操纵基础上学习，部分原因是将复杂的行为与他们引起的场景变化联系起来。我们的洞察力是，以对象数据为中心的观点可以帮助将人类行为和所产生的环境变化联系起来，从而改善多任务策略学习。在这项工作中，我们在环境中构建了一个潜在的空间来建模对象负担 - 定义其用途的对象的属性，然后学习一项政策以实现所需的负担。通过对可变范围任务进行建模和预测所需的负担，我们的方法通过以对象为中心的游戏（PLATO）预测潜在的负担能力，在2D和3D对象操纵模拟和现实世界环境中，对各种相互作用的复杂操作任务的现有方法优于现有方法。可以在我们的网站上找到视频：https：//tinyurl.com/4U23HWFV
Constructing a diverse repertoire of manipulation skills in a scalable fashion remains an unsolved challenge in robotics. One way to address this challenge is with unstructured human play, where humans operate freely in an environment to reach unspecified goals. Play is a simple and cheap method for collecting diverse user demonstrations with broad state and goal coverage over an environment. Due to this diverse coverage, existing approaches for learning from play are more robust to online policy deviations from the offline data distribution. However, these methods often struggle to learn under scene variation and on challenging manipulation primitives, due in part to improperly associating complex behaviors to the scene changes they induce. Our insight is that an object-centric view of play data can help link human behaviors and the resulting changes in the environment, and thus improve multi-task policy learning. In this work, we construct a latent space to model object affordances -- properties of an object that define its uses -- in the environment, and then learn a policy to achieve the desired affordances. By modeling and predicting the desired affordance across variable horizon tasks, our method, Predicting Latent Affordances Through Object-Centric Play (PLATO), outperforms existing methods on complex manipulation tasks in both 2D and 3D object manipulation simulation and real world environments for diverse types of interactions. Videos can be found on our website: https://tinyurl.com/4u23hwfv
