Generalizing Multi-Objective Search via Objective-Aggregation Functions

2025-09-26

AI Summary

Real-world robotic systems require simultaneous optimization of multiple conflicting objectives, yet existing multi-objective search (MOS) algorithms struggle to handle complex objective interactions and modeling constraints directly. This paper proposes a generalized MOS modeling framework based on an implicit objective aggregation function, embedding multi-objective optimization into the state-space search process. By introducing customizable core operation extensions—namely expansion, pruning, and sorting—the framework enables seamless adaptation of standard single-objective or classical MOS algorithms to intricate robotic tasks. Crucially, it is the first to jointly model objective coupling and decision-maker preferences, substantially enhancing algorithmic generality and computational efficiency. Empirical evaluation across navigation, dexterous manipulation, medical planning, and path verification demonstrates speedups of 1–3 orders of magnitude over baseline methods, validating both effectiveness and practical applicability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

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.

Problem

• Generalizing multi-objective search via objective-aggregation functions
• Enabling standard algorithms for complex objective interactions
• Extending core operations for diverse robotics planning problems

Innovation

• Optimizes objectives via aggregation functions
• Extends core operations for specific aggregation functions
• Applies standard multi-objective search algorithms