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Toward Self-Aware Robots
Raja Chatila1, Erwan Renaudo1,2, Mihai Andries1,3
1Institute of Intelligent Systems and Robotics, Sorbonne Université, CNRS, Paris, France.
Frontiers in Robotics and AI
|January 27, 2021
Summary
Robots and AI lack true understanding, acting like "zombies." This paper proposes self-awareness as key for artificial intelligence agents to comprehend their environment and actions, enabling genuine learning and cognition.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- Current robots and AI systems perform tasks without genuine understanding or self-awareness.
- Deep-Learning AI lacks the ability to grasp the meaning of its inputs and outputs.
- A theoretical framework for robot understanding, cognition, and self-awareness is missing.
Purpose of the Study:
- To propose that agent self-awareness is a prerequisite for environmental understanding in artificial agents.
- To explore the emergence of self-awareness from environmental interaction and self-other distinction.
- To outline a cognitive architecture integrating perception, learning, interaction, and decision-making.
Main Methods:
- Conceptual framework development.
- Analysis of agent perception and environmental interaction.
- Exploration of learning mechanisms for actions.
- Examination of agent-human interaction dynamics.
- Investigation of decision-making processes.
- Design of a cognitive architecture.
Main Results:
- Self-awareness emerges from an agent's understanding of its environment and its own embodiment.
- Distinguishing self from environment is crucial for developing self-awareness.
- A unified cognitive architecture can integrate key AI capacities.
Conclusions:
- Achieving genuine understanding and cognition in AI requires developing self-awareness.
- Self-awareness is not innate but emerges through interaction and learning.
- This work provides a theoretical foundation for building more cognizant AI agents.
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