Fast, High-Quality Dual-Arm Rearrangement in Synchronous, Monotone Tabletop Setups

Shome, Rahul; Solovey, Kiril; Yu, Jingjin; Bekris, Kostas E.; Halperin, Dan

doi:10.48550/arxiv.1810.12202

Cited by 3 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed framework can also be leveraged toward efficiently solving simultaneous task and motion planning for many robot manipulators (Dobson and Bekris, 2015). The demonstrated applications to manipulators also motivate dual-arm rearrangement challenges (Shome et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning

et al. 2019

Self Cite

View full text Add to dashboard Cite

Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-quality paths in such scenarios can be achieved, in principle, by exploring the composite space of all robots. Sampling-based planners do so by building a roadmap or a tree data structure in the corresponding configuration space and can achieve asymptotic optimality. The hardness of motion planning, however, renders the explicit construction of such structures in the composite space of multiple robots impractical. This work proposes a scalable solution for such coupled multi-robot problems, which provides desirable path-quality guarantees and is also computationally efficient. In particular, the proposed dRRT * is an informed, asymptotically-optimal extension of a prior sampling-based multi-robot motion planner, dRRT. The prior approach introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space. This work identifies the conditions for convergence to optimal paths in multi-robot problems, which the prior method was not achieving.A. Dobson, R. Shome and K.

show abstract

Section: Discussionmentioning

confidence: 99%

dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overlapping starts and goals complicates the problem as it reduces to the feedback vertex set (FVS) problem [6], which is possibly APX-hard. Dual arm rearrangement allows for parallelism but complicates reasoning [3]. Integer programming is often applied for deciding the object order together with motion planning [2], [3].…”

Section: Related Workmentioning

confidence: 99%

“…Dual arm rearrangement allows for parallelism but complicates reasoning [3]. Integer programming is often applied for deciding the object order together with motion planning [2], [3]. Optimal tabletop placement has also been approached via answer set programming (ASP) [4], [5] and informed heuristics [7].…”

Section: Related Workmentioning

confidence: 99%

Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search

Wang¹,

Gao²,

Nakhimovich³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions do not occur but object-object collisions have to be avoided. The objective is minimizing the number of object transfers under the assumption that the robot can manipulate one object at a time. An efficiently computable decomposition of the configuration space is used to create a "region graph", which classifies all continuous paths of equivalent collision possibilities. Based on this compact but rich representation, a complete dynamic programming primitive DFSDP performs a recursive depth first search to solve monotone problems quickly, i.e., those instances that do not require objects to be moved first to an intermediate buffer. DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer. This work utilizes these primitives as local planners in an informed search framework for more general, non-monotone instances. The search utilizes partial solutions from the primitives to identify the most promising choice of objects and buffers. Experiments demonstrate that the proposed solution returns near-optimal paths with higher success rate, even for challenging non-monotone instances, than other leading alternatives.

show abstract

“…Clearly a challenging task and motion planning problem in the general setting [17], even the combinatorial aspect of object rearrangement is shown to be computationally hard in multiple problems in seemingly simple setups [15]. A multi-arm rearrangement problem is recently explored [20]. In a more abstract setting, multi-object rearrangement has also been studied under the PushPush line of problems [7,8].…”

Section: Introductionmentioning

confidence: 99%

Rubik Tables and Object Rearrangement

Szegedy

2020

Preprint

Self Cite

View full text Add to dashboard Cite

There are n ≥ 2 stacks, each filled with d items (its full capacity), and one empty stack with capacity d. A robot arm, in one stack operation (move), may pop one item from the top of a non-empty stack and subsequently push it into a stack that is not at capacity. In a labeled problem, all nd items are distinguishable and are initially randomly scattered in the n stacks. The items must be rearranged using pop-and-push moves so that at the end, the k th stack holds items (k − 1)d + 1, . . . , kd, in that order, from the top to the bottom for all 1 ≤ k ≤ n. In an unlabeled problem, the nd items are of n types of d each. The goal is to rearrange items so that items of type k are located in the k th stack for all 1 ≤ k ≤ n. In carrying out the rearrangement, a natural question is to find the least number of required pop-and-push moves.In terms of the required number of moves for solving the rearrangement problems, the labeled and unlabeled version have lower bounds Ω(nd + nd log d log n ) and Ω(nd), respectively. Our main contribution is the design of an algorithm with a guaranteed upper bound of O(nd) for both versions when d ≤ cn for arbitrary fixed positive number c. In addition, a subroutine for a problem that we call the Rubik table problem is of independent interest, with applications to problems including multi-robot motion planning.

show abstract

Fast, High-Quality Dual-Arm Rearrangement in Synchronous, Monotone Tabletop Setups

Cited by 3 publications

References 46 publications

dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning

dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning

Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search

Rubik Tables and Object Rearrangement

Contact Info

Product

Resources

About