[Paper Review] Taming Combinatorial Challenges in Optimal Clutter Removal Tasks

Wei Tang, Jingjin Yu|arXiv (Cornell University)|May 31, 2019

Robotic Path Planning Algorithms29 references4 citations

TL;DR

This paper addresses the NP-hard combinatorial challenge of computing optimal object removal sequences for clutter removal using a mobile manipulator robot, proposing dynamic programming-based asymptotically optimal algorithms that scale to 40 objects and fast greedy heuristics that achieve near-optimal performance with significantly reduced computation time, especially in random clutter scenarios with non-overlapping objects.

ABSTRACT

We examine an important combinatorial challenge in clearing clutter using a mobile robot equipped with a manipulator, seeking to compute an optimal object removal sequence for minimizing the task completion time, assuming that each object is grasped once and then subsequently removed. On the structural side, we establish that such an optimal sequence can be NP-hard to compute, even when no two objects to be removed have any overlap. Then, we construct asymptotically optimal and heuristic algorithms for clutter removal. Employing dynamic programming, our optimal algorithm scales to 40 objects. On the other hand, for random clutter, fast greedy algorithms tend to produce solutions comparable to these generated by the optimal algorithm.

Motivation & Objective

• To analyze the computational complexity of optimal clutter removal sequences in robotic task and motion planning.
• To develop scalable algorithms for computing high-quality removal sequences that minimize task completion time.
• To evaluate the trade-offs between optimality and computational efficiency in both single- and multi-exit clutter removal scenarios.

Proposed method

• A backtracking-based dynamic programming algorithm is developed for the single-exit case, enabling resolution-complete and asymptotically optimal solutions for up to 40 objects.
• Best-first and greedy heuristic algorithms are proposed for fast, near-optimal solutions under practical, randomized clutter settings.
• A Voronoi-based partitioning technique is introduced for multi-exit scenarios to improve search efficiency and solution quality.
• The algorithms are evaluated on synthetic clutter scenes with varying object overlap, orientation, and spatial distribution.
• Exhaustive search with dynamic programming is used as a baseline to validate the performance of heuristic methods.
• Empirical evaluation compares computation time and total travel cost across multiple algorithm variants under diverse clutter configurations.

Experimental results

Research questions

• RQ1 Is the problem of finding an optimal object removal sequence in cluttered environments computationally intractable, even when objects do not overlap?
• RQ2 Can dynamic programming-based algorithms achieve asymptotic optimality for clutter removal with a limited number of objects?
• RQ3 How do greedy and best-first heuristic algorithms compare in solution quality and computational efficiency on random clutter configurations?
• RQ4 What is the impact of multiple exits on the search space and solution quality in clutter removal planning?
• RQ5 Can Voronoi-based partitioning improve the scalability and performance of greedy algorithms in multi-exit clutter removal?

Key findings

• The clutter removal problem is proven to be NP-hard even in the planar case with no overlapping objects, establishing its fundamental computational intractability.
• The dynamic programming-based exhaustive search algorithm successfully computes optimal solutions for up to 40 objects within practical computation time.
• Greedy algorithms achieve total travel costs nearly identical to optimal solutions across all tested scenarios, including non-overlapping and overlapping object configurations.
• The Voronoi-based greedy algorithm demonstrates superior scalability in multi-exit settings, outperforming both basic greedy and exhaustive search in computation time while maintaining high solution quality.
• Centered clutter configurations with dense object placement significantly increase computation time due to higher branching factors, making them more challenging than uniformly scattered scenes.
• For random clutter, greedy methods with longer planning horizons (e.g., multi-step or MCTS variants) yield slightly better solutions than basic greedy, at the cost of increased computation.