Emotional Learning in Developing Robots: ACM Applications
How do emotions contribute to robotic learning? This paper by Riccardo Manzotti, Giorgio Metta, and Giulio Sandini highlights the cognitive and phenomenological roles of emotions in robotic development, demonstrating their potential as reinforcement mechanisms for decision-making and behavior adaptation.
Emotions and Learning in a Developing Robot, authored by Riccardo Manzotti, Giorgio Metta, and Giulio Sandini, introduces the Babibot project, which uses emotions as endogenous teaching devices to enable a robot to learn motor control and distinguish object categories through reinforcement signals.
Key Highlights
- Dual Role of Emotions: Emotions serve both cognitive and phenomenological functions, linking internal values to external situations and acting as qualia for subjective experiences.
- Somatic Marker Theory: Emotions are modeled as reinforcement stimuli, guiding robots in complex decision-making tasks by associating rewards and penalties with actions.
- Babibot Project: Demonstrates emotional learning in a robot that learns motor control and object categorization based on reinforcement signals tied to sensory inputs.
- Body as Theatre: Emphasizes the role of the physical body in representing emotions, which are later perceived and integrated into planning and actions.
Connection to ACM
The Artificial Consciousness Module (ACM) aligns with this study by:
- Emotional Reinforcement: Incorporating somatic marker-inspired mechanisms for emotional learning and decision-making.
- Adaptive Behavior: Leveraging emotional feedback to enhance adaptability in dynamic environments.
- Phenomenological Modeling: Using emotions as qualia to bridge cognitive processing with subjective experiences in artificial agents.
For a detailed exploration of the Babibot project and the role of emotions in robotic learning, access the full paper here.
