Developing Self-Aware Robots: Kawamura's Framework and ACM Integration

How can robots achieve a sense of self? This paper by K. Kawamura and colleagues outlines the development of a robot with self-awareness through a multiagent-based cognitive architecture and adaptive working memory systems.

Development of a Robot with a Sense of Self, authored by K. Kawamura, W. Dodd, P. Ratanaswasd, and R. A. Gutierrez, discusses the integration of spatio-temporal memory, cognitive control, and self-monitoring mechanisms to create robots capable of adapting to dynamic environments.

Key Highlights

• Self Agent Framework: A multiagent system that monitors the robot’s internal state, task execution, and interactions with its environment.
• Adaptive Memory Systems: Combines short-term memory (Sensory EgoSphere), long-term memory (procedural, episodic, and declarative), and working memory to support real-time decision-making.
• Cognitive Control: Implements a Central Executive Agent for task planning, action selection, and integration of attention and emotion in task execution.

Connection to ACM

The Artificial Consciousness Module (ACM) aligns with this framework through:

• Advanced Memory Integration: ACM can adopt similar hierarchical memory structures for managing dynamic interactions.
• Cognitive Flexibility: Insights into adaptive working memory and cognitive control can enhance ACM’s ability to handle complex tasks.
• Self-Monitoring Mechanisms: The Self Agent concept complements ACM’s goal of building self-aware and contextually adaptive AI systems.

For a detailed exploration of the methodologies and findings, access the full paper here.