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Papers/PackIt: A Virtual Environment for Geometric Planning

PackIt: A Virtual Environment for Geometric Planning

Ankit Goyal, Jia Deng

2020-07-21ICML 2020 1Robot Task Planning
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Robot Task PlanningPackItAverage Reward64.9PackNN
Robot Task PlanningPackItAverage Reward59.2Heuristic Largest First-Aligned-BLBF
Robot Task PlanningPackItAverage Reward49.4Heuristic Largest First-Aligned-Random
Robot Task PlanningPackItAverage Reward41.9Heuristic Random-Aligned-BLBF

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