Fast High-Quality Tabletop Rearrangement in Bounded Workspace (2110.12325v1)

Abstract: 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. source:github.com/arc-l/TRLB

• The paper proposes TRLB, an innovative method that decouples inherent and acquired constraints for efficient tabletop rearrangement.
• The methodology employs lazy buffer allocation and bidirectional search to significantly reduce computational overhead in cluttered workspaces.
• Experimental results show higher success rates and faster performance compared to state-of-the-art planners in dense, real-world scenarios.

Fast High-Quality Tabletop Rearrangement in Bounded Workspace

The paper "Fast High-Quality Tabletop Rearrangement in Bounded Workspace" addresses a challenging problem in robotics, namely the rearrangement of objects on a tabletop using overhand grasps within a constrained workspace. The authors focus on enabling intelligent robotic manipulation by proposing efficient solutions that overcome the limitations imposed by cluttered environments.

Problem Overview

The tabletop object rearrangement problem, known as TORO, is characterized by the need to move multiple objects from start to goal poses, potentially requiring temporary placements to achieve the final arrangement. This problem is NP-hard due to the inherent combinatorial constraints induced by object collisions when transitioning between configurations. Allocating buffer locations optimally for temporary object storage in a cluttered workspace introduces additional acquired constraints, further complicating the search space.

Methodology

The authors present a novel approach, Tabletop Rearrangement with Lazy Buffers (TRLB), which efficiently solves TORI instances by decoupling inherent and acquired constraints. TORI refers to the rearrangement problem that uses internal buffers within the workspace. The TRLB framework operates in several key stages:

1. Primitive Plan Computation: A primitive plan that addresses only the inherent constraints of the dependency graph is generated under the assumption of unlimited external buffer space.
2. Lazy Buffer Allocation: Using the primitive plan as guidance, this step involves selecting buffer locations within the workspace without explicitly checking all constraints upfront. This lazy allocation strategy delays detailed feasibility checks, significantly reducing computational overhead.
3. High-Level Search: The framework utilizes forward and bidirectional search trees to handle partial plans resulting from buffer allocation failures. This allows recovery from such failures through randomness in selecting and revisiting nodes within the search trees.
4. Preprocessing Routine: Designed to reduce running buffers, preprocessing simplifies the dependency graph and converts complex instances into monotone rearrangement problems, enhancing scalability in dense environments.

Experimental Results

The proposed TRLB framework demonstrates significant advancements over existing state-of-the-art methods, such as BiRRT(fmRS) and MCTS planners, across various object configurations and density levels. The TRLB method achieves higher success rates, solution quality, and computational efficiency. Notably, the application of preprocessing significantly accelerates the solution process for dense and large-scale scenarios.

Practical Implications and Future Work

The efficiency of TRLB is underscored by its application potential in both home and industrial robotic automation settings. The robust handling of dense and cluttered environments foreshadows its utility in real-world scenarios where adaptive and intelligent manipulation is required. Future advancements could explore extensions into more dynamic environments, incorporating online learning for real-time adaptability in rearrangement tasks. Additionally, integration with advanced perception systems could further enhance the framework's applicability in unstructured settings.

In conclusion, this work epitomizes the synthesis of theoretical insights and practical application in the field of robotic manipulation, offering a promising direction for the deployment of intelligent robotic systems capable of high-quality object rearrangements.