Examining the Effectiveness of Obfuscatory Planning Strategies through Human Observation

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Authors: Dakota Sullivan, David J. Porfirio, Bilge Mutlu, Laura M. Hiatt
Cited by: 0
References: 19

Abstract

Robots are increasingly relied upon for task completion in privacy-critical human environments. In these environments, it is imperative that a robot's potentially sensitive goals remain obfuscated. To address this need, a substantial amount of literature has proposed methods for obfuscatory task planning. These works make many attempts to experimentally or analytically determine whether agents can conceal their goals from observers. While these works make guarantees that resulting plans will conceal an agent's goals, they are often only theoretical. Within this work, we develop three obfuscatory task planning strategies inspired by prior literature to evaluate with human observers (N = 160). Our preliminary results show that observers struggle to identify a robot's goals at similar levels regardless of whether obfuscatory or optimal task planning strategies are employed. These findings call into question the purported benefits of many obfuscatory task planning strategies.

Fields of Study & Subjects

AI-based Problem Solving and Planning Robotic Path Planning Algorithms Privacy-Preserving Technologies in Data

Publication details

Journal / source
ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Pages
619–623
Publication date
12 March 2026
Language
EN