Explaining the Concept of Deep Reinforcement Learning
FANUC is Utilizing Technological Advancements to Improve Machine Learning
FANUC is working on the commercial implementation of an algorithm that could lead to total self-maintenance for our appliances and contraptions.
FANUC factory robots in Tokyo have used deep reinforcement learning to figure out how to move objects from one box to another. Typically, this would require extensive programming with time-consuming trial and error. However, a deep learning model—or neural network—enables robots to learn tasks on its own overnight. Without any programming in place, it only took the robotic arm 8 hours to have a 90% performance rate – the same percentage as a robotic arm programmed by an expert.
Don’t worry, robots won’t be taking over the world anytime soon, but deep machine learning may soon be a reality in many industrial workplaces. Efficiency and productivity at factories could shoot through the roof thanks to significant advances in artificial intelligence.
Deep Machine Learning: A Definition
Whenever you use a search engine or smart phone, chances are you’ve come into contact with a deep learning machine. Between speech recognition and now facial structures, your phone is a powerful tool, capable of learning on its own. FANUC is using the same technology your phones and computers have in order to recognize and organize your pictures, sort email and spam, and scan checks.
Instead of making your life more entertaining with face filters, FANUC’s use of deep machine learning will allow machines to perform tasks without a human needing to program it to tell it what to do.
How Deep Machine Learning Works
Humans have the ability to recognize animals, places, and plants without much effort at all. Even a five year old can tell a fish apart from a dog, but computers don’t find the psychology so easy – yet. Deep machine learning relies on an algorithm based on how the human brain works. The biological brain has individual cells called neurons. When recognizing something like a fish, these neurons work independently to decide what they’re seeing, but they also compare their findings to the findings of other neurons to reach a conclusion.
Deep machine learning uses Artificial Neural Networks to mimic this process. Artificial neural networks are built in a step by step procedure:
- Tiny mathematical formulas are grouped together, like neurons.
- These groupings are called a “net” and are instructed to work together to “learn”.
- If these groupings are expanded upon, the bigger and deeper a net gets.
- The net learns from practice the machine performs.
Instead of relying on human beings taking hours to write complex, tedious instructions for machines to perform tasks at 60% accuracy rates, programmers can now use simple formulas and instructions to program a machine to learn to solve problems based on examples or test runs. This gives machines the ability to be more intelligent than if we were to write a program for them, as the programming could always be flawed.
FANUC in the Workplace: The Importance of Teamwork and Teaching
With social media, online shopping, medical facilities, and self-driving cars already utilizing deep machine learning’s technology, FANUC has the potential to create workplaces using machines able to work at proficiencies unable to be equaled by human beings.
Part of how FANUC is working to develop this is by putting their focus on teamwork and teaching. FANUC understands if machines are able to work together in a system to perform a task, they can learn from each other, resulting in higher workplace efficiency.
For instance, one robotic arm took eight hours to learn a task, but if eight robotic arms had been working together with the ability communicate, the task could have been learned with the same success rate in about an hour’s time. If a factory had one hundred arms working together as a team, teaching each other and learning from one another, their precision and speed could lead to one of the highest efficiencies ever seen in an industrial workplace. Time will tell.
Perfect FANUC Robot Operation Has Never Been More Important
Although the applications for which FANUC robots and machines are used may changes, the machines themselves must be running at perfect efficiency to be able to operate at such an advanced level. Improper and erratic function leads to not only an inefficient and expensive production process, but negatively affect the way FANUC robots continue to refine their neural processes and deep learning.
Tri Star CNC Services is your go-to for all FANUC component repairs to ensure CNC machines and robots are functioning flawlessly with no loss in productivity. Whether your FANUC robots operate by manual programming or deep reinforcement learning, Tri Star CNC Services provides the best servo drive, spindle amp, power supply module and more repair services in the upper Midwest and beyond. Browse our full supply of FANUC parts available for sale or exchange.