Space-Aware Reconfiguration

Halperin, Dan; Kreveld, Marc van; Miglioli-Levy, Golan; Sharir, Micha

doi:10.48550/arxiv.2006.04402

Cited by 2 publications

(2 citation statements)

References 18 publications

(44 reference statements)

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“…In [13], a complete algorithm is developed that reasons about object retrieval, rearranging other objects as needed, with later work [34] considering plan optimality and sensor occlusion. While objectives in most problems focus on the number of motions, [35] seeks to minimize the space needed to carry out a rearrangement task for discs moving inside the workspace.…”

Section: Related Workmentioning

confidence: 99%

On Minimizing the Number of Running Buffers for Tabletop Rearrangement

Gao¹,

Feng²,

Yu³

2021

Robotics: Science and Systems XVII

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For tabletop object rearrangement problems with overhand grasps, storage space which may be inside or outside the tabletop workspace, or running buffers, can temporarily hold objects which greatly facilitates the resolution of a given rearrangement task. This brings forth the natural question of how many running buffers are required so that certain classes of tabletop rearrangement problems are feasible. In this work, we examine the problem for both the labeled (where each object has a specific goal pose) and the unlabeled (where goal poses of objects are interchangeable) settings. On the structural side, we observe that finding the minimum number of running buffers (MRB) can be carried out on a dependency graph abstracted from a problem instance, and show that computing MRB on dependency graphs is NP-hard. We then prove that under both labeled and unlabeled settings, even for uniform cylindrical objects, the number of required running buffers may grow unbounded as the number of objects to be rearranged increases; we further show that the bound for the unlabeled case is tight. On the algorithmic side, we develop highly effective, exact algorithms for finding MRB for both labeled and unlabeled tabletop rearrangement problems, scalable to over a hundred objects under very high object density. More importantly, our algorithms also compute a sequence witnessing the computed MRB that can be used for solving object rearrangement tasks. Employing these algorithms, empirical evaluations reveal that random labeled and unlabeled instances, which more closely mimics real-world setups, generally have fairly small MRBs.

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Section: Related Workmentioning

confidence: 99%

On Minimizing the Number of Running Buffers for Tabletop Rearrangement

Gao¹,

Feng²,

Yu³

2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

show abstract

“…In [14], a complete algorithm is developed that reasons about object retrieval, rearranging other objects as needed, with later work [35] considering plan optimality and sensor occlusion. While the objectives in most problems focus on the number of motions, Halperin et al [36] examined spaceaware reconfiguration in which disc objects move along straight lines in the workspace, seeking to minimize space needed to carry out a rearrangement task.…”

Section: Introductionmentioning

confidence: 99%

On Minimizing the Number of Running Buffers for Tabletop Rearrangement

Gao¹,

Feng²,

Yu³

2021

Preprint

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For tabletop rearrangement problems with overhand grasps, storage space outside the tabletop workspace, or buffers, can temporarily hold objects which greatly facilitates the resolution of a given rearrangement task. This brings forth the natural question of how many running buffers are required so that certain classes of tabletop rearrangement problems are feasible. In this work, we examine the problem for both the labeled (where each object has a specific goal pose) and the unlabeled (where goal poses of objects are interchangeable) settings. On the structural side, we observe that finding the minimum number of running buffers (MRB) can be carried out on a dependency graph abstracted from a problem instance, and show that computing MRB on dependency graphs is NPhard. We then prove that under both labeled and unlabeled settings, even for uniform cylindrical objects, the number of required running buffers may grow unbounded as the number of objects to be rearranged increases; we further show that the bound for the unlabeled case is tight. On the algorithmic side, we develop highly effective algorithms for finding MRB for both labeled and unlabeled tabletop rearrangement problems, scalable to over a hundred objects under very high object density. Employing these algorithms, empirical evaluations show that random labeled and unlabeled instances, which more closely mimics real-world setups, have much smaller MRBs.

show abstract

Space-Aware Reconfiguration

Cited by 2 publications

References 18 publications

On Minimizing the Number of Running Buffers for Tabletop Rearrangement

On Minimizing the Number of Running Buffers for Tabletop Rearrangement

On Minimizing the Number of Running Buffers for Tabletop Rearrangement

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