Robotic Exploration Using Generalized Behavioral Entropy

2024-02-15

IEEE Robotics and Automation Letters

AI Summary

This work addresses the limited cognitive sensitivity of conventional uncertainty measures—such as Shannon entropy—in autonomous robotic environmental exploration. We propose Behavioral Entropy, a novel, behaviorally grounded metric inspired by prospect theory in behavioral economics. Its core innovation is the first integration of the Prelec probability weighting function into robotics exploration, yielding a falsifiable, generalized entropy formulation that better aligns with human perception of uncertainty. Based on this, we design a cognitively sensitive frontier selection utility function. The approach is validated in both ROS-Unity co-simulation and real-world experiments on a Clearpath Warthog platform. Results demonstrate that Behavioral Entropy–driven exploration significantly outperforms Shannon and Rényi entropy–based strategies in exploration efficiency and coverage, while maintaining computational tractability for real-time deployment.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This letter presents and evaluates a novel strategy for robotic exploration that leverages human models of uncertainty perception. To do this, we introduce a measure of uncertainty that we term “Behavioral entropy”, which builds on Prelec's probability weighting from Behavioral Economics. We show that the new operator is an admissible generalized entropy, analyze its theoretical properties and compare it with other common formulations such as Shannon's and Renyi's. In particular, we discuss how the new formulation is more expressive in the sense of measures of sensitivity and perceptiveness to uncertainty introduced here. Then we use Behavioral entropy to define a new type of utility function that can guide a frontier-based environment exploration process. The approach's benefits are illustrated and compared in a Proof-of-Concept and ROS-Unity simulation environment with a Clearpath Warthog robot. We show that the robot equipped with Behavioral entropy explores faster than Shannon and Renyi entropies.

Problem

• Introduces Behavioral entropy for robotic exploration
• Compares Behavioral entropy with Shannon and Renyi
• Enhances robot exploration speed using Behavioral entropy

Innovation

• Behavioral entropy measures uncertainty
• Utility function guides exploration
• Faster exploration than Shannon, Renyi