Rearranging similar objects with a manipulator using pebble graphs

Krontiris, Athanasios; Shome, Rahul; Dobson, Andrew; Kimmel, Andrew; Bekris, Kostas E.

doi:10.1109/humanoids.2014.7041499

Cited by 32 publications

(21 citation statements)

References 16 publications

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“…There are recent approaches for rearrangement that can deal with non-monotone instances under certain conditions [21], [11], [10], [22], [23]. One method simplifies the problem by requiring that all objects are unlabeled, i.e., interchangeable and can occupy any pose in a final arrangement [22].…”

Section: Related Workmentioning

confidence: 99%

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

Krontiris

Bekris

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Manipulating multiple movable obstacles is a hard problem that involves searching high-dimensional C-spaces. A milestone method for this problem was able to compute solutions for monotone instances. These are problems where every object needs to be transferred at most once to achieve a desired arrangement. The method uses backtracking search to find the order with which objects should be moved. This paper first proposes an approximate but significantly faster alternative for monotone rearrangement instances. The method defines a dependency graph between objects given minimum constraint removal paths (MCR) to transfer each object to its target. From this graph, the approach discovers the order of moving objects by performing topological sorting without backtracking search. The approximation arises from the limitation to consider only MCR paths, which minimize, however, the number of conflicts between objects. To solve non-monotone instances, this primitive is incorporated in a higher-level incremental search algorithm for general rearrangement planning, which operates similar to Bi-RRT. Given a start and a goal object arrangement, tree structures of reachable new arrangements are generated by using the primitive as an expansion procedure. The integrated solution achieves probabilistic completeness for the general non-monotone case and based on simulated experiments it achieves very good success ratios, solution times and path quality relative to alternatives.

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Section: Related Workmentioning

confidence: 99%

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

Krontiris

Bekris

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…Monotone instances for such problems, where each obstacle may be moved at most once, are easier [29]. Recent work has focused on "nonmonotone" instances [32], [33], [34], [35], [36], [37]. Rearrangement with overlaps considered in the current paper includes "non-monotone" instances although other aspects of the problem are relaxed.…”

Section: Contribution Relative To Prior Workmentioning

confidence: 99%

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

Han

Stiffler

Krontiris

et al. 2018

The International Journal of Robotics Research

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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.

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“…Object Rearrangement using Prehensile Actions: Most prior works on objects rearrangement focused on pickand-place actions for transporting objects on collision-free paths [1]. One proposed algorithm adapts a manipulation approach for clearing a path to an unreachable object [19], and uses a tree-search with backtracking for finding a feasible order in which objects can be picked and placed [2].…”

Section: Related Workmentioning

confidence: 99%

“…Solving rearrangement problems optimally is computationally prohibitive. Therefore, most proposed algorithms content with finding feasible plans [1]- [3]. The few efficient and optimal algorithms that have been proposed require additional constraints, such as having a clear buffer zone for intermediate placement of objects [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Song

Boularias

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collision-free grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic end-effectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the end-effector's trajectories.

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Rearranging similar objects with a manipulator using pebble graphs

Cited by 32 publications

References 16 publications

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

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

Object Rearrangement with Nested Nonprehensile Manipulation Actions

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