Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation

Cheong, Sang Hun; Cho, Brian Y.; Lee, Jinhwi; Kim, ChangHwan; Nam, Changjoo

doi:10.1109/icra40945.2020.9197485

Cited by 38 publications

(21 citation statements)

References 16 publications

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“…A closely related problem is object retrieval. A fast and complete algorithm has been proposed to determine obstacles to be relocated before retrieving a target [33], with an extension that studies where to relocate obstacles [34]. Often the objective is to minimize the number of actions until the target is accessible [35], [36].…”

Section: Related Workmentioning

confidence: 99%

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Wang¹,

Miao²,

Bekris³

2021

Preprint

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Prehensile object rearrangement in cluttered and confined spaces has broad applications but is also challenging. For instance, rearranging products in a grocery or home shelf means that the robot cannot directly access all objects and has limited free space. This is harder than tabletop rearrangement where objects are easily accessible with top-down grasps, which simplifies robot-object interactions. This work focuses on problems where such interactions are critical for completing tasks and extends state-of-the-art results in rearrangement planning. It proposes a new efficient and complete solver under general constraints for monotone instances, which can be solved by moving each object at most once. The monotone solver reasons about robot-object constraints and uses them to effectively prune the search space. The new monotone solver is integrated with a global planner to solve non-monotone instances with high-quality solutions fast. Furthermore, this work contributes an effective pre-processing tool to speed up arm motion planning for rearrangement in confined spaces. The pre-processing tool provide significant speed-ups (49.1% faster on average) in online query resolution. Comparisons in simulations further demonstrate that the proposed monotone solver, equipped with the pre-processing tool, results in 57.3% faster computation and 3 times higher success rate than alternatives. Similarly, the resulting global planner is computationally more efficient and has a higher success rate given the more powerful monotone solver and the pre-processing tool, while producing high-quality solutions for non-monotone instances (i.e., only 1.3 buffers are needed on average). Videos of demonstrating solutions on a real robotic system and codes can be found at https://github.com/Rui1223/uniform object rearrangement.

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Section: Related Workmentioning

confidence: 99%

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Wang¹,

Miao²,

Bekris³

2021

Preprint

View full text Add to dashboard Cite

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“…Buffer Identification: In rearrangement problems, collision-free locations are needed for obstacle displacements. Some previous efforts predetermine buffer candidates and place obstacles to accessible candidate locations when necessary [5], [20]. Others decouple the original problem into successive subproblems [5], [14].…”

Section: Related Workmentioning

confidence: 99%

Fast High-Quality Tabletop Rearrangement in Bounded Workspace

Gao¹,

Lau²,

Huang³

et al. 2021

Preprint

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In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions ("buffers") to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the "lazy" planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches.

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“…In this case, the goal g is the robot's end-effector pose w ee T g , and Eq. ( 3) can be rewritten in a simplified form ||f fk pq T q ´w i T g || 2 2 ď ξ goal . In the second phase when the robot is opening the door, f task p¨q maps VKC's joint space to the joint of the door's revolute axis.…”

Section: B Goalsmentioning

confidence: 99%

“…There exists research efforts focusing on each of the above challenges, i.e., motion planning in confined space [1][2][3], control or learning frameworks for opening doors and drawers [4][5][6][7], and whole-body planning for foot-arm coordination [8][9][10]. However, a unified approach that tackles all three challenges altogether in household settings is still mostly missing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations

Jiao¹,

Zhang²,

Jiang³

et al. 2021

Preprint

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We construct a Virtual Kinematic Chain (VKC) that readily consolidates the kinematics of the mobile base, the arm, and the object to be manipulated in mobile manipulations. Accordingly, a mobile manipulation task is represented by altering the state of the constructed VKC, which can be converted to a motion planning problem, formulated and solved by trajectory optimization. This new VKC perspective of mobile manipulation allows a service robot to (i) produce well-coordinated motions, suitable for complex household environments, and (ii) perform intricate multi-step tasks while interacting with multiple objects without an explicit definition of intermediate goals. In simulated experiments, we validate these advantages by comparing the VKC-based approach with baselines that solely optimize individual components. The results manifest that VKC-based joint modeling and planning promote task success rates and produce more efficient trajectories.

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Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation

Cited by 38 publications

References 16 publications

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Fast High-Quality Tabletop Rearrangement in Bounded Workspace

Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations

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