This work 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.

翻译：本文提出并评估了一种利用人类不确定性感知模型的新型机器人探索策略。为此，我们引入了一种称为“行为熵”的不确定性度量方法，该方法建立在行为经济学中普雷莱克概率加权理论基础之上。我们证明了这种新算子是一种可接受的广义熵，分析了其理论特性，并将其与香农熵、瑞利熵等其他常用公式进行了比较。特别地，我们讨论了新公式在本文所引入的敏感性和感知性度量意义上更具表达能力。随后，我们利用行为熵定义了一种新型效用函数，可引导基于前沿的环境探索过程。通过概念验证实验和基于ROS-Unity的仿真环境，以Clearpath Warthog机器人为平台，我们展示并比较了该方法的优势。结果表明，配备行为熵的机器人比使用香农熵和瑞利熵的机器人探索速度更快。