# Scalarizing Multi-Objective Robot Planning Problems using Weighted Maximization

arXiv: https://arxiv.org/abs/2312.07227 · HTML: https://mlsanity.com/paper/2312.07227/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [Generalizing Multi-Objective Search via Objective-Aggregation Functions](https://mlsanity.com/paper/2509.22085/) (2025)
- [Demonstration-Enhanced Adaptable Multi-Objective Robot Navigation](https://mlsanity.com/paper/2404.04857/) (2024)
- [Multi-Objective Planning with Contextual Lexicographic Reward Preferences](https://mlsanity.com/paper/2502.10476/) (2025)
- [Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization](https://mlsanity.com/paper/2605.12771/) (2026)

## Abstract (arXiv)

When designing a motion planner for autonomous robots there are usually multiple objectives to be considered. However, a cost function that yields the desired trade-off between objectives is not easily obtainable. A common technique across many applications is to use a weighted sum of relevant objective functions and then carefully adapt the weights. However, this approach may not find all relevant trade-offs even in simple planning problems. Thus, we study an alternative method based on a weighted maximum of objectives. Such a cost function is more expressive than the weighted sum, and we show how it can be deployed in both continuous- and discrete-space motion planning problems. We propose a novel path planning algorithm for the proposed cost function and establish its correctness, and present heuristic adaptations that yield a practical runtime. In extensive simulation experiments, we demonstrate that the proposed cost function and algorithm are able to find a wider range of trade-offs between objectives (i.e., Pareto-optimal solutions) for various planning problems, showcasing its advantages in practice.