Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments

Area of Science:

• Artificial Intelligence
• Robotics
• Cognitive Science

Background:

• Contemporary artificial intelligence (AI) research increasingly views intelligence as an emergent property of embodied agents interacting with their environments.
• Designing sophisticated AI behaviors necessitates understanding the functional coupling between agents and their surroundings.
• This paradigm shift moves beyond purely computational models to encompass physical embodiment and environmental interaction.

Purpose of the Study:

• To investigate the application of reservoir computing as an efficient method for analyzing behavior emergence in agent-environment interactions.
• To develop and present reservoir computing models inspired by imitation learning principles.
• To extract key components of behavior resulting from dynamic agent-environment interactions.

Main Methods:

• Utilized reservoir computing, a type of recurrent neural network, for modeling complex dynamic systems.
• Employed imitation learning-inspired designs to guide the reservoir computing model.
• Implemented and tested the models on a mobile robot platform to study real-world interaction dynamics.

Main Results:

• Demonstrated the efficacy of reservoir computing in capturing behavior that emerges from agent-environment interactions.
• Successfully extracted essential behavioral components through the developed imitation learning-inspired models.
• Validated the proposed learning architectures through experimental results on a mobile robot.

Conclusions:

• Reservoir computing offers an efficient and effective tool for studying emergent behaviors in embodied AI.
• The integration of imitation learning principles enhances the ability of reservoir computing to model interaction dynamics.
• Experimental validation confirms the potential of these architectures for advancing the understanding of AI behavior.