AI or Human? Understanding Perceptions of Embodied Robots with LLMs

2025-07-22

AI Summary

This study investigates human perceptual capability in attributing intelligence—autonomous AI versus human teleoperation—to embodied robots during interactive tasks. Method: We conducted the first systematic Turing test in real-world embodied robotics settings, employing information retrieval and package handover tasks to compare participant discrimination performance between large language model–driven autonomous robots and human-teleoperated robots. Experiments were performed under static and dynamic conditions using double-blind human–robot trials, integrating autonomous navigation and natural multimodal interaction. Contribution/Results: Participants’ classification accuracy did not significantly exceed chance level (p > 0.05), indicating that current embodied AI behaviors exhibit high behavioral human-likeness. We identified motion fluency, response timing, and task adaptability as three critical perceptual dimensions governing human judgment. These findings provide empirical grounding for explainable embodied intelligence and computational modeling of human–robot trust.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The pursuit of artificial intelligence has long been associated to the the challenge of effectively measuring intelligence. Even if the Turing Test was introduced as a means of assessing a system intelligence, its relevance and application within the field of human-robot interaction remain largely underexplored. This study investigates the perception of intelligence in embodied robots by performing a Turing Test within a robotic platform. A total of 34 participants were tasked with distinguishing between AI- and human-operated robots while engaging in two interactive tasks: an information retrieval and a package handover. These tasks assessed the robot perception and navigation abilities under both static and dynamic conditions. Results indicate that participants were unable to reliably differentiate between AI- and human-controlled robots beyond chance levels. Furthermore, analysis of participant responses reveals key factors influencing the perception of artificial versus human intelligence in embodied robotic systems. These findings provide insights into the design of future interactive robots and contribute to the ongoing discourse on intelligence assessment in AI-driven systems.

Problem

• Investigates perception of intelligence in embodied robots using Turing Test
• Assesses ability to distinguish AI- vs human-operated robots in interactive tasks
• Explores factors influencing perception of artificial vs human intelligence in robots

Innovation

• Used LLMs in embodied robots for Turing Test
• Assessed perception and navigation in interactive tasks
• Analyzed factors influencing human-AI intelligence perception