Cankut Bora Tuncer

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

Cankut Bora Tuncer, Marc Toussaint, Ozgur S. Oguz

ICRA 2026, Vienna / Austria, Under Review

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 minimise 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. 

Supplementary videos and code are available at: https://sites.google.com/view/mo-segman/.

SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments

Cankut Bora Tuncer, Dilruba Sultan Haliloglu, Ozgur S. Oguz

IROS 2025, Hangzhou / China

In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimisation-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals, enhancing overall efficiency. Furthermore, proposed generalizable heuristics guide the forward search in a more targeted manner. Extensive evaluations in maze-like tasks populated with numerous objects and obstacles demonstrate that SeGMan is capable of generating not only consistent and computationally efficient manipulation plans but also outperforming the state-of-the-art approaches. Check more here: https://sites.google.com/view/segman-lira/

H-MaP: An Iterative and Hybrid Sequential Manipulation Planner

Berk Cicek, Arda Sarp Yenicesu, Cankut Bora Tuncer, Kutay Demiralp, Ozgur S. Oguz

RA-L 2025, To be presented in ICRA26

This paper introduces H-MaP, a hybrid sequential manipulation planner that addresses complex tasks requiring both sequential actions and dynamic contact mode switches. Our approach reduces configuration space dimensionality by decoupling object trajectory planning from manipulation planning through object-based waypoint generation, informed contact sampling, and optimisation-based motion planning. This architecture enables handling of challenging scenarios involving tool use, auxiliary object manipulation, and bimanual coordination. Experimental results across seven diverse tasks demonstrate H-MaP’s superior performance compared to existing methods, particularly in highly constrained environments where traditional approaches fail due to local minima or scalability issues. The planner’s effectiveness is validated through both simulation and real-robot experiments. 

Check more here: https: //sites.google.com/view/h-map/