Metasensor: A Proposal for Sensor Evolution in Robotics

Area of Science:

• Robotics
• Artificial Intelligence
• Evolutionary Computation

Background:

• Biological organisms exhibit complex behaviors driven by sophisticated sensory systems.
• Current artificial agents often lack environmental adaptability due to overlooked sensor roles.
• Robots frequently show poor environmental adaptation compared to biological counterparts.

Purpose of the Study:

• To formalize a novel architectural component, the metasensor, for enabling sensor evolution in artificial agents.
• To demonstrate how metasensors can enhance robot adaptability to new tasks and dynamic environments.
• To validate the metasensor concept through a proof-of-concept implementation.

Main Methods:

• Proposed a metasensor layer that optimizes input signal interpretation for robotic agents.
• Implemented the metasensor using both hand-coded logic and a neural network substrate.
• Employed evolutionary algorithms to evolve neural network weights for sensor interpretation.

Main Results:

• The metasensor successfully modified robot behavior, transitioning from light avoidance to area avoidance.
• Both hand-coded and evolved neural network implementations validated the metasensor's adaptive capabilities.
• Demonstrated the potential for sensor evolution to alter robot behavior effectively.

Conclusions:

• The metasensor offers a pathway for robots to adapt to diverse tasks and environments without hardware/software redesign.
• Sensor evolution via metasensors shows significant promise for real-world robotic applications.
• This approach facilitates online adaptation in robots operating in dynamic, unknown environments.