Cybernetics is the Only Way Robots Can Achieve Human Intelligence

by Jayanti October 1, 2022

Cybernetics will drive the future of robotics by empowering them with human intelligence

Robotics Industry is constantly rising in this automation world. According to reports, Indian industrial robotics market is predicted to grow at a CAGR of 13.3% between 2019-2024. With its rising industry applications and productivity benefits, the study of cybernetics is likely to be a vital element in the advancement of robotics.

The craving for gadgets or machines that can keep up with the challenges of the present world and largely function in simpler and smarter ways is evident. Automation and autonomy have offered this by producing and delivering products and services that contain the least amount of human intervention, making certain jobs more convenient than ever before even when information is incomplete and uncertain. The appearance of new service robots and their wide evolution into new applications has further facilitated the world of automation. Due to the dynamic nature of robotics, numerous application sectors are now using robotics to perform predetermined tasks and enhance human efforts in both physical and analytical ways. Robotics has enhanced task efficiency, dependability, and quality, all of which were earlier, products of a laborious procedure. Being a critical component of automation, robotics is currently used in an ever-growing variety of fields, like manufacturing, transportation, healthcare & medical care, utilities, defense, facilities, operations, and more recently, information technology. Here Cybernetics enters as a primary element as robots need to be advanced.

What is Cybernetics and what makes it different from Artificial Intelligence (AI)?

Cybernetics is a study of science that focuses on developing technologies that act or think like humans by researching how electrical devices or machines and the human brain function to enhance the value of the job to be performed. Cybernetics is the best workaround physical embodiment of Artificial Intelligence (AI), Machine Learning (ML), and predictive analysis and control, investigating underlying systems/structures, possibilities, and limitations of complex mechanisms, including robotics, and generating an autonomous environment that uses minimal to no human interaction. AI and cybernetics are two dissimilar perspectives on intelligent systems or systems that may act to achieve an aim. Making computers imitate intelligent behavior using pre-stored world representations is the primary goal of AI. In general, cybernetics tells us how systems control themselves and can take actions autonomously based on environmental signals even when the information is minimal and subject to significant uncertainty or noise. These systems go beyond simple computation; they can also control biological (body temperature regulation), mechanical (engine speed regulation), social (managing a huge workforce), and economic (controlling a national economy) systems.

How does Cybernetics work?

Every cybernetic system’s aim is to be set up so that its operations are linked in a variety of input-output system configurations which are normally driven with reference control signs. This is achieved by processing feedback-based automatic closed-loop control systems that can decide which behaviors should be changed, which actions should be tracked, how to compare the actions to the reference, and how to adapt the application behaviors in the most effective way . In natural cybernetic systems, this regulatory mechanisms generates or organizes by itself with the help of self-learning. On the other hand, artificial cybernetic systems behave or are influenced by human-implemented automatic control systems. Essential elements of cybernetic systems are sensors, the controller, actuators and the system to be controlled.

Cybernetics in robotics

Cybernetics in robotics systems’ main objective is to use AI and machine learning in the sense-plan-act paradigm normally used to develop robots so they can operate productively in real-world scenarios. Developing a robot to understand and differentiate complex situations every day is highly demanding and getting the situation awareness correctly identified is crucial to ensuring the desired reference control signal can be identified for implementation. This can make sure an industrial robot recognizes and picks up the correct item for the next stage of the manufacturing process from a selection of parts to ensure the requests of the human to be served a variety of beverages will get the correct drink. Sensors and sensor systems that are perfectly calibrated are necessary for ensuring the situation awareness is achieved perfectly in real-time using AI-based models which can be learned and applied in various situations such as driverless cars, medical robots, automated manufacturing, and home care robots.

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