Probing an Embodied LLM: When Higher Observation Fidelity Hurts Problem Solving
Large Language Models (LLMs) are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain success or failure in closed-loop embodied tasks. Following an empirical AI methodology, we study an embodied LLM agent behaviorally by varying the available information and measuring the resulting changes in behavior. Using the Lockbox, a sequential mechanical puzzle with hidden interdependencies, we evaluate LLMs across RGB, RGB-D, and ground-truth symbolic observations in a physical robotic setup and use simulation to probe the resulting behavior. Counterintuitively, agents perform best under raw RGB input and worst under p...
| Authors | Oussama Zenkri, Oliver Brock |
|---|---|
| Primary category | cs.AI |
| Published | 19 May 2026 |
| Last updated at source | 1 October 2026 |
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