Mobile Manipulation Planning for Tabletop Rearrangement

TL;DR

This paper presents an improved mobile manipulation planner for tabletop rearrangement that reduces unnecessary robot movements and enhances plan quality and efficiency through strategic operation grouping and state re-exploration.

Contribution

It introduces a novel planning strategy enabling multiple operations from a single position and incorporates state re-exploration, improving over previous methods in efficiency and solution quality.

Findings

Outperforms existing planners in solution quality

Reduces planning time significantly

Enables multiple operations from a single robot position

Abstract

Efficient tabletop rearrangement planning seeks to find high-quality solutions while minimizing total cost. However, the task is challenging due to object dependencies and limited buffer space for temporary placements. The complexity increases for mobile robots, which must navigate around the table with restricted access. A*-based methods yield high-quality solutions, but struggle to scale as the number of objects increases. Monte Carlo Tree Search (MCTS) has been introduced as an anytime algorithm, but its convergence speed to high-quality solutions remains slow. Previous work~\cite{strap2024} accelerated convergence but required the robot to move to the closest position to the object for each pick and place operation, leading to inefficiencies. To address these limitations, we extend the planner by introducing a more efficient strategy for mobile robots. Instead of selecting the nearest available location for each action, our approach allows multiple operations (e.g., pick-and-place) from a single standing position, reducing unnecessary movement. Additionally, we incorporate state re-exploration to further improve plan quality. Experimental results show that our planner outperforms existing planners both in terms of solution quality and planning time.