ABSTRACT

The rapid advancement of robotics and automation demands a fundamental rethinking of how artificial agents perceive, process, and respond to human interaction. This presentation introduces a novel theoretical and applied framework for developing embodied AI consciousness in robotic systems, drawing upon interdisciplinary insights from cognitive science, linguistics, and educational technology.

Building on our recent research on adaptive token boundaries and human chunking mechanisms in multimodal large language models (LLMs), this study proposes a phased approach to functional organization in robotic cognition. The framework integrates three core components: (1) Adaptive Sensory Fusion, enabling robots to process multimodal inputs (visual, auditory, tactile) in ways that mirror human perceptual chunking; (2) Embodied Learning Architecture, grounding AI cognition in physical interaction through continuous feedback loops; and (3) Intersubjective Communication Protocols, facilitating natural and context-aware human-robot dialogue.

We present empirical findings from our Educational Metaverse platform and VR-based training systems, demonstrating how tactile intelligence and mixed reality environments can enhance robotic learning and human-robot collaboration. Specifically, we showcase applications in educational robotics, where AI-driven systems adapt to learner behavior through real-time cognitive modeling.

The presentation concludes with a discussion of ethical considerations, the role of cultural context in human-robot interaction design, and future directions for achieving functional consciousness in next-generation robotic systems. This research contributes to the conference theme by bridging theoretical innovation with practical applications in robotics and automation.

Keywords: Embodied AI, Human-Robot Interaction, Multimodal LLMs, Tactile Intelligence, Educational Robotics, AI Consciousness, Sensory Fusion, Adaptive Learning