Machine Learning And Robots

Robots have become indispensable in today’s industry – be it in consumer electronics or the automotive industry. However, most robot solutions are still “old school” ones. This means they do not act intelligently and cannot work directly with people.

The next stage in robot evolution is co-bots – “collaborative robots”. These collaborative robots can work directly with human employees without protective devices. Co-bots are still a relatively recent development. However, this has been taking off at breakneck speed for a few years – with revolutionary steps for the world of work and even society. Co-bots combined with machine learning are the solution.

Co-bots Learn With The Help Of Machine Learning

First of all, the definition, because machine learning and AI are sometimes equated. Machine learning is a subfield of artificial intelligence. Within machine learning, there is still deep learning. This method uses neural networks to analyze large data sets. However, the focus here is on machine learning and its advantages for co-bots. They use machine learning algorithms to develop their skills independently.

Of course, this requires a large number of training and application phases. One could say: learning in different situations that co-bots are confronted with. In this way, the collaborative robot solutions have a flood of other data at their disposal – be it from the environment, the work utensils or the human colleagues. Machine learning helps the co-bots in the learning process: Incoming data is structured and sorted. After that, they are recognized in recurring patterns. Co-bots can thus derive important information and act and react accordingly in an individual situation. Data in its different manifestations form the basis for every action that collaborative robots carry out with the help of machine learning – sure, intelligent and in collaboration with humans. Incidentally, new standards were introduced for this to call themselves co-bots.

It Doesn’t Work Without State-Of-The-Art Sensors

Of course, machine learning alone is not enough. Co-bots also require state-of-the-art sensor technology. This is how you get your flood of data, among other things. The most important “senses” include “feeling” and “seeing”.

The former is made possible by so-called force-torque sensors. These measure forces act on co-bots with the highest possible precision within milliseconds. This is how collaborative robots “feel” and grab a component or exert a certain pressure on a surface. An example shows how “sensitive” cobots can act and react: They can easily hold an egg without damaging it. Or even open it without leaving any shell residue in your scrambled or fried egg. Safe human-robot collaboration is not possible without force-torque sensors.

With the help of visual sensors – in the form of different cameras – co-bots see the world. In this way, they grasp their static and dynamic environment. This makes collisions with people or spontaneously occurring obstacles obsolete. External sensors can even expand “seeing”. Co-bots thus access visual data from internal and external sources. From this, for example, an even more efficient commute for the co-bots can be calculated. An example: The door of a hangar is blocked on the known way from A to B. The co-bot sees this at A before it could have seen the hangar door and independently works out a new route to get to B.

By the way, soon co-bots will also “hear”. Voice controls are already in extended test phases. However, the hearing functionality will only gain importance in the coming years. This massively expands the possible uses of co-bots, especially with machine learning.

Collaborative Robots Combined With Machine Learning Will Move Into Almost All Sectors

Some automotive groups and larger manufacturers of consumer electronics, such as smartphones, appreciate the capabilities of intelligent robots. These industries are already using such robotic solutions as prototypes or as full-fledged mechanical workers. The range of tasks varies: from monotonous and arduous work, such as transporting heavy objects from A to B, to “sensitive” activities, such as attaching security films to smartphone displays. What should not be forgotten in the many possible areas of application of these co-bots: they do not replace human labor. The human being is supported in the tasks but not replaced. The best co-bot doesn’t work without a human colleague.

With the constant further development of machine learning, the areas of responsibility of collaborative robots are becoming more complex. As already mentioned, they are currently learning their activities in many training and application phases. Shortly, co-bots will be able to move freely anywhere, to recognize and process tasks independently. The technical term for this is autonomous learning.

Best Cases Of Intelligent Robots Can Be Found In Medicine

Thanks to modern aerospace technology, the technologies behind co-bots are evolving rapidly. And thanks to the various and highly complex processes in medicine, the population can also see how advanced and important robotics with the help of machine learning is.

Smart robots are already being used in the OR in particular. They support doctors, nurses and patients alike. Collaborative robots are 100 per cent precise. This is particularly beneficial in minimally invasive surgery. Tissue injuries, which generally cannot be avoided with such interventions, are reduced to a minimum. This, in turn, means that patients experience a faster recovery time. Doctors and nurses also experience more rest and break times through co-bots, as the procedures are faster. In addition, the medical staff can spend more time looking after the patients.

Machine learning in the right combination with technological achievements shows how much it can change various industries and even society for the better. However, in this area, it will remain important to emphasize that human workers will not be lost. Co-bots support people, not replace them. By the way: These advanced, intelligent robot solutions are also usually cheaper than the standard robots, most of which are still used in many industrial sectors.

Also Read: How Intelligent Can AI Be?

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