Cybernetics, interoception, and the art of embodiment

Problem

The paper addresses a gap in the capability of artificial agents to operate autonomously and adaptively by leveraging biological principles. It emphasizes the need for embodied agents that can monitor and respond to their internal states, a feature that is often overlooked in current AI systems. The work is presented as a preprint, indicating that it has not yet undergone peer review.

Method

The authors propose a novel framework that integrates concepts from cybernetics, reinforcement learning, and neuroscience. Key components of the framework include:

• Internal Environment: The framework incorporates interoceptive inputs, allowing embodied AI systems to monitor their internal states, akin to biological organisms.
• Feedback Loop: A closed feedback loop is formalized, enabling self-monitoring and adaptive responses based on internal state changes.
• Internal States: These states are modeled as stable contextual variables that significantly influence the learning and decision-making processes of the agents.

Results

The available text does not report quantitative results.

Limitations

The authors acknowledge that the practical application of their proposed framework in advancing robotics and physical AI has yet to be demonstrated. This limitation suggests that while the theoretical foundation is laid out, empirical validation and real-world implementation remain challenges that need to be addressed.

Why it matters

This work has significant implications for the development of more sophisticated autonomous systems that can mimic biological adaptability. By incorporating interoception and self-monitoring, future research could lead to embodied agents that are not only reactive but also proactive in their environments, potentially enhancing their utility in various applications such as robotics, healthcare, and human-computer interaction.