论文标题
技能前提的关系学习
Relational Learning for Skill Preconditions
论文作者
论文摘要
为了确定是否可以在任何给定环境中执行技能，机器人需要学习技能的先决条件。随着机器人在动态和非结构化环境中开始运行，前提模型将需要概括为具有不同形状和尺寸的对象数量。在这项工作中，我们专注于在不受约束的环境中学习操纵技能的先决条件模型。我们的工作是由直觉的动机，即许多具有多个对象的复杂的操纵任务可以通过专注于较不复杂的成对对象关系来简化。我们提出了一个对象关系模型，该模型了解这些成对对象关系的连续表示。我们的对象关联模型经过仿真训练，一旦学习，单独的先决条件模型就使用了一旦学习，以预测现实世界任务的技能前提。我们对$ 3 $不同的操作任务进行评估的前提模型：扫描，切割和拆除。我们表明，我们的方法在跨不同形状和大小的对象跨对象的所有3个任务预测前提条件方面有了重大改进。
To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured environments, precondition models will need to generalize to variable number of objects with different shapes and sizes. In this work, we focus on learning precondition models for manipulation skills in unconstrained environments. Our work is motivated by the intuition that many complex manipulation tasks, with multiple objects, can be simplified by focusing on less complex pairwise object relations. We propose an object-relation model that learns continuous representations for these pairwise object relations. Our object-relation model is trained completely in simulation, and once learned, is used by a separate precondition model to predict skill preconditions for real world tasks. We evaluate our precondition model on $3$ different manipulation tasks: sweeping, cutting, and unstacking. We show that our approach leads to significant improvements in predicting preconditions for all 3 tasks, across objects of different shapes and sizes.
