Neuroscience-Inspired Cognitive Architecture for Ethical Collaborative Robots

Abstract

We present a cognitive architecture inspired by neuroscience principles for ethical collaborative robots. The architecture combines saccadic attention allocation (3-7 hop graph traversal at 4 Hz), Hebbian learning for online adaptation (10,887 learned edges), spreading activation across a 124,024-node semantic network, and architectural-level ethics enforcement through 500 moral cases and 8 symbiotic principles.

Unlike existing cobot architectures that treat safety as constraint satisfaction, our approach integrates ethical reasoning at the architectural level — the robot cannot act without passing through the moral reasoning pipeline. We propose a 15-month experimental validation framework in collaboration with a university robotics laboratory.

Honest Limitations: Architecture implemented in software only; physical robot testing is proposed, not completed. ETHICS benchmark: 70.7% (n=2000). Consciousness metrics defined but philosophical validity debated.