6 accepted papers
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized sign
We study a problem of multi-agent exploration with behaviorally heterogeneous robots. Each robot maps its surroundings using SLAM and identifies a set of areas of interest (AoIs) or frontiers that are the most informative to explore next. The robots assess the utility of going to a frontier using <i
Entropy-based objectives are widely used to perform state space exploration in reinforcement learning (RL) and dataset generation for offline RL. Behavioral entropy (BE), a rigorous generalization of classical entropies that incorporates cognitive and perceptual biases of agents, was recently propos…
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 th
The effective deployment of robots in risky and crowded environments (RCE) requires the specification of robot plans that are consistent with humans' behaviors. As is well known, humans perceive uncertainty and risk in a biased way, which can lead to a diversity of actions and expectations when inte
This work investigates the design of risk-perception-aware motion-planning strategies that incorporate non-rational risk associated with uncertain spatial costs. Our proposed method employs the Cumulative Prospect Theory (CPT) to generate a perceived risk map over a given environment. CPT-like perce