PLATO: Predicting Latent Affordances Through Object-Centric Play-猫眼课题宝

DOI：

10.48550/arxiv.2203.05630

发表时间：

2022-03

期刊：

影响因子：

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中科院分区：

其他

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作者：

Suneel Belkhale;Dorsa Sadigh

以可扩展的方式构建多样化的操作技能库仍然是机器人技术中尚未解决的挑战。解决这一挑战的一种方法是非结构化的人类游戏，即人类在环境中自由运作以达到未指定的目标。Play是一种简单而廉价的方法，用于收集不同的用户演示，并在环境中覆盖广泛的状态和目标。由于这种多样化的覆盖范围，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鲁棒。然而，这些方法通常难以在场景变化和具有挑战性的操作原语下学习，部分原因是将复杂行为与它们引起的场景变化不正确地关联起来。我们的观点是，以对象为中心的游戏数据视图可以帮助将人类行为与环境中的变化联系起来，从而改善多任务策略学习。在这项工作中，我们构建了一个潜在的空间来模拟对象的启示-定义其用途的对象的属性-在环境中，然后学习一个策略来实现所需的启示。通过建模和预测所需的启示在可变的地平线任务，我们的方法，预测潜在的启示通过以对象为中心的游戏（PLATO），优于现有的方法在复杂的操作任务，在2D和3D对象操作模拟和真实的世界环境中的各种类型的交互。视频可以在我们的网站上找到：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

PLATO: Predicting Latent Affordances Through Object-Centric Play

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