Case Study

Evolutionary AI Transforms Robotic Actuation

Harnessing the Power of Sensor Fusion for Enhanced Efficiency


In the rapidly evolving field of robotics, efficiency and resource utilization are paramount. This case study explores a project where two AI methodologies were employed to decrease the energy cost of actuating a robot without modifying its hardware. The results demonstrate the superiority of Evolutionary AI over Conventional AI in terms of energy efficiency, resource utilization, and quality of actuation.



Dynamic learning using Liquid Time-Constant Networks (LTCs), a class of dynamic neural network.

Utilized 5 neurons

Consumed 0.34W of energy


Convolutional Neural Network (CNN), A type of Deep Learning model

Utilized 150 neurons

Consumed 200W of energy​


Efficiency and Resource Utilization

Evolutionary AI consumed

less energy than Conventional AI
0 x

Evolutionary AI USED

fewer resources (neurons) than Conventional AI
0 x

Qualitative Observations

Robotic movements under Evolutionary AI were “smoother” and “more natural” compared to Conventional AI


Evolutionary AI in Robotics

Solves hard optimization and design problems, delivering high-quality solutions

Produces novel and efficient designs and strategies unlikely to be conceived by human experts

Adapts to changing situations and environments

Considers the whole robot at once, enabling holistic exploitation of robot features


This case study highlights the potential of Evolutionary AI in transforming robotic actuation. By achieving the same actuation using significantly fewer resources and less energy, Evolutionary AI demonstrates its efficiency and effectiveness over Conventional AI. The project successfully met the client’s goal of decreasing energy cost without changing hardware, showcasing the real-world applications of Evolutionary AI in robotics.

The findings suggest that Evolutionary AI is a promising approach for efficient and effective robotic actuation, offering advantages in energy efficiency, resource utilization, adaptability, and quality of actuation. As the field of robotics continues to advance, the intelligent selection and optimization of AI models will be crucial in developing cutting-edge, sustainable, and high-performing robotic systems.


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