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

2025-11-03

AI Summary

This paper addresses the problem of jointly planning sequences and guided manipulations for multi-object rearrangement in highly constrained environments. We propose a novel multi-objective optimization framework that minimizes the number of replanning iterations, robot travel distance, and dependency graph violations. To tackle critical obstacle relocation in non-monotonic, highly cluttered scenarios, our method integrates selective guided forward search (SGFS), a lazy dependency maintenance mechanism, and an adaptive sub-goal selection strategy. Evaluated on nine benchmark tasks, our approach consistently produces feasible solutions, reduces average solving time significantly, and yields higher-quality plans than baseline methods—demonstrating robustness, computational efficiency, and scalability.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Plans object rearrangement in constrained environments efficiently
• Minimizes robot travel distance and unnecessary replanning actions
• Handles cluttered scenarios by relocating only critical obstacles

Innovation

• Lazy evaluation method preserves dependency structures
• Selective Guided Forward Search relocates critical obstacles
• Refinement method eliminates unnecessary pick-and-place actions