Vector Cost Behavioral Planning for Autonomous Robotic Systems with Contemporary Validation Strategies

TL;DR

This paper introduces an expanded vector cost bimatrix game approach for multi-objective decision making in autonomous robots, validated through comprehensive simulations and explainable AI tools, outperforming scalar methods.

Contribution

It extends vector cost game methods to multiple objectives, integrating XAI and state-space exploration for robust, interpretable robotic decision-making frameworks.

Findings

Vector cost method outperforms scalar weighted sum approaches.

Explainable AI aids in high-dimensional decision analysis.

Comprehensive simulation pipeline validates the approach.

Abstract

The vector cost bimatrix game is a method for multi-objective decision making that enables autonomous robotic systems to optimize for multiple goals at once while avoiding worst-case scenarios in neglected objectives. We expand this approach to arbitrary numbers of objectives and compare its performance to scalar weighted sum methods during competitive motion planning. Explainable Artificial Intelligence (XAI) software is used to aid in the analysis of high dimensional decision-making data. State-space Exploration of Multidimensional Boundaries using Adherence Strategies (SEMBAS) is applied to explore performance modes in the parameter space as a sensitivity study for the baseline and proposed frameworks. While some works have explored aspects of game theoretic planning and intelligent systems validation separately, we combine each of these into a novel and comprehensive simulation pipeline. This integration demonstrates a dramatic improvement of the vector cost method over scalarization and offers an interpretable and generalizable framework for robotic behavioral planning. Code available at https://github.com/toazbenj/race_simulation. The video companion to this work is available at https://tinyurl.com/vectorcostvideo.