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

arXiv: https://arxiv.org/abs/2511.01476 · HTML: https://mlsanity.com/paper/2511.01476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly](https://mlsanity.com/paper/2106.02489/) (2021)
- [Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation](https://mlsanity.com/paper/2003.10863/) (2020)
- [Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps](https://mlsanity.com/paper/1711.07369/) (2017)
- [Fast High-Quality Tabletop Rearrangement in Bounded Workspace](https://mlsanity.com/paper/2110.12325/) (2021)
- [Newton methods for k-order Markov Constrained Motion Problems](https://mlsanity.com/paper/1407.0414/) (2014)
- [Minimizing Running Buffers for Tabletop Object Rearrangement: Complexity, Fast Algorithms, and Applications](https://mlsanity.com/paper/2304.01764/) (2023)
- [Toward Optimal Tabletop Rearrangement with Multiple Manipulation Primitives](https://mlsanity.com/paper/2310.00167/) (2023)
- [ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star](https://mlsanity.com/paper/2309.13707/) (2023)
- [High-Quality Tabletop Rearrangement with Overhand Grasps: Hardness Results and Fast Methods](https://mlsanity.com/paper/1705.09180/) (2017)
- [Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons](https://mlsanity.com/paper/2208.02312/) (2022)
- [Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments](https://mlsanity.com/paper/2305.17175/) (2023)
- [Planning for Tabletop Object Rearrangement](https://mlsanity.com/paper/2411.10899/) (2024)
- [Solving Rearrangement Puzzles using Path Defragmentation in Factored State Spaces](https://mlsanity.com/paper/2212.02955/) (2022)
- [Solving Sequential Manipulation Puzzles by Finding Easier Subproblems](https://mlsanity.com/paper/2405.02053/) (2024)
- [SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments](https://mlsanity.com/paper/2503.04409/) (2025)
- [Mobile Manipulation Planning for Tabletop Rearrangement](https://mlsanity.com/paper/2505.18732/) (2025)

## Abstract (arXiv)

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.