High-Quality Tabletop Rearrangement with Overhand Grasps: Hardness Results and Fast Methods

Han, Shuai D.; Stiffler, Nicholas M.; Krontiris, Athanasios; Bekris, Kostas E.; Yu, Jingjin

doi:10.15607/rss.2017.xiii.051

Cited by 17 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article expands an earlier version of this work [7] and includes the following new contributions: (i) a proof showing that cost-optimal unlabelled non-overlapping tabletop rearrangement is NP-hard, (ii) a complete description of ILP-based heuristics and an algorithm for solving the object rearrangement problem with overlap sub-optimally, (iii) significantly enhanced evaluation with experiments conducted on a hardware platform ( Fig. 2d), corroborating the real-world benefits of the proposed method.…”

Section: Introductionmentioning

confidence: 57%

Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps

Han

Stiffler

Krontiris

et al. 2018

The International Journal of Robotics Research

Self Cite

View full text Add to dashboard Cite

This paper studies the underlying combinatorial structure of a class of object rearrangement problems, which appear frequently in applications. The problems involve multiple, similar-geometry objects placed on a flat, horizontal surface, where a robot can approach them from above and perform pickand-place operations to rearrange them. The paper considers both the case where the start and goal object poses overlap, and where they do not. For overlapping poses, the primary objective is to minimize the number of pick-and-place actions and then to minimize the distance traveled by the end-effector. For the non-overlapping case, the objective is solely to minimize the travel distance of the end-effector. While such problems do not involve all the complexities of general rearrangement, they remain computationally hard in both cases. This is shown through reductions from well-understood, hard combinatorial challenges to these rearrangement problems. The reductions are also shown to hold in the reverse direction, which enables the convenient application on rearrangement of well studied algorithms. These algorithms can be very efficient in practice despite the hardness results. The paper builds on these reduction results to propose an algorithmic pipeline for dealing with the rearrangement problems. Experimental evaluation, including hardware-based trials, shows that the proposed pipeline computes high-quality paths with regards to the optimization objectives. Furthermore, it exhibits highly desirable scalability as the number of objects increases in both the overlapping and non-overlapping setup.

show abstract

Section: Introductionmentioning

confidence: 57%

Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps

Han

Stiffler

Krontiris

et al. 2018

The International Journal of Robotics Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…1. It contains the subroutine for solving C-LSR (Poly-C-LSR, in line [4][5][6][7][8][9][10][11][12][13][14], which iteratively separates the left and right objects in each stack (line 12), and consolidate the stacks (line 13). Details are already mentioned in Lemma IV.2.…”

Section: Fundamental Bounds On Optimalitymentioning

confidence: 99%

“…These efforts focus on feasibility and no solution quality arguments have been provided. A recent work has focused, similar to the current paper, on high-quality rearrangement solutions but in the context of manipulation challenges in tabletop environments [11].…”

Section: Introductionmentioning

confidence: 97%

Efficient, High-Quality Stack Rearrangement

Han

Stiffler

Bekris

et al. 2018

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

This work studies rearrangement problems involving the sorting of robots or objects in stack-like containers, which can be accessed only from one side. Two scenarios are considered: one where every robot or object needs to reach a particular stack, and a setting in which each robot has a distinct position within a stack. In both cases, the goal is to minimize the number of stack removals that need to be performed. Stack rearrangement is shown to be intimately connected to pebble motion problems, a useful abstraction in multi-robot path planning. Through this connection, feasibility of stack rearrangement can be readily addressed. The paper continues to establish lower and upper bounds on optimality, which differ only by a logarithmic factor, in terms of stack removals. An algorithmic solution is then developed that produces suboptimal paths much quicker than a pebble motion solver. Furthermore, informed search-based methods are proposed for finding highquality solutions. The efficiency and desirable scalability of the methods is demonstrated in simulation.

show abstract

“…Similar problems arise in robotics. For example, such problems arise when a robot needs to arrange products on a shelf in a store, or when a robot needs to move objects around in order to access a specific product that needs to be picked up; see, e.g., [16][17][18]. In robotics, these problems are often referred to as object rearrangement problems.…”

Section: Introductionmentioning

confidence: 99%

Space-Aware Reconfiguration

Halperin¹,

Kreveld²,

Miglioli-Levy³

et al. 2020

Preprint

View full text Add to dashboard Cite

We consider the problem of reconfiguring a set of physical objects into a desired target configuration, a typical (sub)task in robotics and automation, arising in product assembly, packaging, stocking store shelves, and more. In this paper we address a variant, which we call space-aware reconfiguration, where the goal is to minimize the physical space needed for the reconfiguration, while obeying constraints on the allowable collision-free motions of the objects. Since for given start and target configurations, reconfiguration may be impossible, we translate the entire target configuration rigidly into a location that admits a valid sequence of moves, where each object moves in turn just once, along a straight line, from its starting to its target location, so that the overall physical space required by the start, all intermediate, and target configurations for all the objects is minimized.We investigate two variants of space-aware reconfiguration for the often examined setting of n unit discs in the plane, depending on whether the discs are distinguishable (labeled) or indistinguishable (unlabeled). For the labeled case, we propose a representation of size Opn 4 q of the space of all feasible initial rigid translations, and use it to find, in Opn 6 q time, a shortest valid translation, or one that minimizes the enclosing disc or axis-aligned rectangle of both the start and target configurations. For the significantly harder unlabeled case, we show that for almost every direction, there exists a translation in this direction that makes the problem feasible. We use this to devise heuristic solutions, where we optimize the translation under stricter notions of feasibility. We present an implementation of such a heuristic, which solves unlabeled instances with hundreds of discs in seconds.

show abstract

High-Quality Tabletop Rearrangement with Overhand Grasps: Hardness Results and Fast Methods

Cited by 17 publications

References 54 publications

Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps

Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps

Efficient, High-Quality Stack Rearrangement

Space-Aware Reconfiguration

Contact Info

Product

Resources

About