MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environments

TL;DR

MO-SeGMan is a novel planning framework for complex, constrained rearrangement tasks that optimizes object placement sequences, minimizes replanning and travel distance, and efficiently handles cluttered environments.

Contribution

It introduces MO-SeGMan with a lazy evaluation method, SGFS for obstacle relocation, and adaptive subgoal selection, advancing rearrangement planning in constrained environments.

Findings

Feasible plans generated in all benchmark cases.

Faster solution times compared to baselines.

Superior solution quality in complex scenarios.

Abstract

In this work, we introduce MO-SeGMan, a Multi-Objective Sequential and Guided Manipulation planner for highly constrained rearrangement problems. MO-SeGMan generates object placement sequences that minimize both replanning per object and robot travel distance while preserving critical dependency structures with a lazy evaluation method. To address highly cluttered, non-monotone scenarios, we propose a Selective Guided Forward Search (SGFS) that efficiently relocates only critical obstacles and to feasible relocation points. Furthermore, we adopt a refinement method for adaptive subgoal selection to eliminate unnecessary pick-and-place actions, thereby improving overall solution quality. Extensive evaluations on nine benchmark rearrangement tasks demonstrate that MO-SeGMan generates feasible motion plans in all cases, consistently achieving faster solution times and superior solution quality compared to the baselines. These results highlight the robustness and scalability of the proposed framework for complex rearrangement planning problems.