# Generalizing Multi-Objective Search via Objective-Aggregation Functions

arXiv: https://arxiv.org/abs/2509.22085 · HTML: https://mlsanity.com/paper/2509.22085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Sampling-Based Motion Planning: A Comparative Review](https://mlsanity.com/paper/2309.13119/) (2023)
- [Provably Safe Robot Navigation with Obstacle Uncertainty](https://mlsanity.com/paper/1705.10907/) (2017)
- [Approximate bi-criteria search by efficient representation of subsets of the Pareto-optimal frontier](https://mlsanity.com/paper/2006.10302/) (2020)
- [Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles](https://mlsanity.com/paper/2003.07811/) (2020)
- [Scenario-Based Motion Planning with Bounded Probability of Collision](https://mlsanity.com/paper/2307.01070/) (2023)
- [Scalarizing Multi-Objective Robot Planning Problems using Weighted Maximization](https://mlsanity.com/paper/2312.07227/) (2023)
- [A Lexicographic Search Method for Multi-Objective Motion Planning](https://mlsanity.com/paper/1909.02184/) (2019)
- [Inspection planning under execution uncertainty](https://mlsanity.com/paper/2309.06113/) (2023)

## Built on by (most cited first, 3 of 3)

- [Multi-Objective Search: Algorithms, Applications, and Emerging Directions](https://mlsanity.com/paper/2510.25504/) (2025)
- [Bridging the Evaluation Gap: Standardized Benchmarks for Multi-Objective Search](https://mlsanity.com/paper/2603.24084/) (2026)
- [Approximate Multi-Objective Search Under Rulebooks](https://mlsanity.com/paper/2608.04398/) (2026)

## Abstract (arXiv)

Multi-objective search (MOS) has become essential in robotics, as real-world robotic systems need to simultaneously balance multiple, often conflicting objectives. Recent works explore complex interactions between objectives, leading to problem formulations that do not allow the usage of out-of-the-box state-of-the-art MOS algorithms. In this paper, we suggest a generalized problem formulation that optimizes solution objectives via aggregation functions of hidden (search) objectives. We show that our formulation supports the application of standard MOS algorithms, necessitating only to properly extend several core operations to reflect the specific aggregation functions employed. We demonstrate our approach in several diverse robotics planning problems, spanning motion-planning for navigation, manipulation and planning fr medical systems under obstacle uncertainty as well as inspection planning, and route planning with different road types. We solve the problems using state-of-the-art MOS algorithms after properly extending their core operations, and provide empirical evidence that they outperform by orders of magnitude the vanilla versions of the algorithms applied to the same problems but without objective aggregation.