Insect-inspired AI for autonomous robots

Area of Science:

• Robotics
• Artificial Intelligence
• Bio-inspired Computing

Background:

• Autonomous robots require advanced artificial intelligence (AI) for complex tasks, but onboard computing limitations hinder autonomy, especially for smaller robots.
• The approaching end of Moore's Law further challenges traditional AI approaches in robotics.
• Insect intelligence presents a promising, resource-efficient alternative due to its inherent parsimony in power and mass.

Purpose of the Study:

• To explore insect intelligence as a viable alternative for AI in small, autonomous mobile robots.
• To identify key aspects of insect intelligence (embodiment, sensory-motor coordination, swarming) that enable resource efficiency.
• To assess the current state and future challenges of insect-inspired AI in robotics, particularly for navigation.

Main Methods:

• Review and discussion of core principles of insect intelligence relevant to AI parsimony.
• Analysis of insect-inspired AI's application to robotic tasks like navigation.
• Evaluation of suitable processor architectures, including traditional and neuromorphic options.

Main Results:

• Insect intelligence offers significant advantages in power and mass efficiency for robotic AI.
• Key insect traits like embodiment, sensory-motor coordination, and swarming are crucial for resource-efficient AI.
• Insect-inspired AI shows potential for navigation and other robotic tasks, with ongoing challenges for widespread adoption.

Conclusions:

• Insect-inspired AI is a promising direction for achieving greater autonomy in small mobile robots.
• Exploiting natural insect intelligence is essential for maximizing the efficiency of AI on both traditional and neuromorphic processors.
• Further research is needed to overcome challenges and fully integrate insect-inspired AI into robotic systems.