论文标题
包装：几何规划的虚拟环境
PackIt: A Virtual Environment for Geometric Planning
论文作者
论文摘要
共同理解对象的几何形状和操纵它们的行动的能力对于智能代理至关重要。我们将这种能力称为几何规划。最近，已经提出了许多交互式环境来评估各种技能的智能代理，但是，它们都不符合几何规划的需求。我们介绍了Packit，这是一种虚拟环境，可以评估和可能学习进行几何计划的能力，在该环境中，代理需要采取一系列动作将一组对象包装到有限空间的盒子中。我们还使用进化算法构建了一组具有挑战性的包装任务。此外，我们研究了包括基于无模型的基于启发式和基于启发式的方法的任务的各种基准，以及基于搜索的优化方法，这些方法采用了访问环境模型的访问。代码和数据可在https://github.com/princeton-vl/packit上找到。
The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. We refer to this ability as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. We present PackIt, a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space. We also construct a set of challenging packing tasks using an evolutionary algorithm. Further, we study various baselines for the task that include model-free learning-based and heuristic-based methods, as well as search-based optimization methods that assume access to the model of the environment. Code and data are available at https://github.com/princeton-vl/PackIt.
