Embodied artificial cognition: evaluating self-awareness in multimodal large language models with robotic sensory integration

Dellibarda Varela, Iñaki Luciano ORCID: https://orcid.org/0009-0002-7028-6924 (2025). Embodied artificial cognition: evaluating self-awareness in multimodal large language models with robotic sensory integration. Tesis (Master), E.T.S.I. Industriales (UPM).

Descripción

Título: Embodied artificial cognition: evaluating self-awareness in multimodal large language models with robotic sensory integration
Autor/es:
Director/es:
Tipo de Documento: Tesis (Master)
Título del máster: Automática y Robótica
Fecha: 23 Septiembre 2025
Materias:
ODS:
Palabras Clave Informales: Self-Awareness, Multimodal Large Language Models (MM-LLMs), Structural Equation Modeling (SEM), Ablation Test, Multi-Dimensional Awareness, Cognitive Robotics & Past-Present Memory.
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Otro
Licencias Creative Commons: Ninguna

Texto completo

[thumbnail of TFM_I_L_D_V.pdf] PDF (Portable Document Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (33MB)

Resumen

Self-Awareness --- the capacity of an individual to represent and understand itself as the subject of experience and action --- is sustained as the foundation of intelligence and autonomous behavior. The most recent advances in AI have reached human-like performance in tasks that integrate multimodal information, especially in large language models (LLMs), which has raised interest in the embodiment capabilities of AI agents in non-human platforms such as robots.

For centuries, different fields of study, from philosophy to neuroscience, have devoted significant efforts to the definition and characterization of Self-Awareness. In the present study, the capabilities of a LLM to develop Self-Awareness are analyzed when embedded in an autonomous mobile robot, relying solely on sensorimotor experience.

By integrating a multimodal LLM into an autonomous mobile robot, we test its capacity to achieve artificial Self-Awareness. We find that the system demonstrates solid environmental awareness, self-recognition, and predictive awareness, which allows it to infer its robotic nature and movement characteristics. Structural Equation Modeling (SEM) reveals how sensory integration influences different dimensions of Self-Awareness and its coordination with past–present memory, as well as the hierarchical internal associations that drive self-identification. Moreover, through SEM we identify similarities between the cognitive constructs developed by the system and the human brain structures responsible for Self-Awareness.

Ablation tests of sensory inputs identify critical modalities for each dimension, demonstrate compensatory interactions between sensors, and confirm the essential role of structured episodic memory in coherent reasoning. These findings show that, given adequate sensory information about the world and itself, multimodal LLMs exhibit emergent Self-Awareness, opening the door to embodied artificial cognitive systems.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Sin especificar
-
Sin especificar
Sin especificar
Sin especificar

Más información

ID de Registro: 91258
Identificador DC: https://oa.upm.es/91258/
Identificador OAI: oai:oa.upm.es:91258
Depositado por: Iñaki Dellibarda
Depositado el: 02 Oct 2025 09:01
Ultima Modificación: 02 Oct 2025 09:01