Papers › PackIt: A Virtual Environment for Geometric Planning
PackIt: A Virtual Environment for Geometric Planning
Ankit Goyal, Jia Deng
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.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Robot Task Planning | PackIt | PackNN | Average Reward | 64.9 | #1 of 4 | Archive leaderboard | report |
| Robot Task Planning | PackIt | Heuristic Largest First-Aligned-BLBF | Average Reward | 59.2 | #2 of 4 | Archive leaderboard | report |
| Robot Task Planning | PackIt | Heuristic Largest First-Aligned-Random | Average Reward | 49.4 | #3 of 4 | Archive leaderboard | report |
| Robot Task Planning | PackIt | Heuristic Random-Aligned-BLBF | Average Reward | 41.9 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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