5 applications of machine learning in Robotics

applications of ML in Robotics

As we are building our way to the digital era, most of you heard about this buzz word called “Machine Learning”, and as mentioned in the Google trends, interest in applications of ML in Robotics is not changed much over the last 3 years.

A growing number of businesses worldwide are using transformative capabilities of machine learning, mainly when applied to robotic systems in the place of work.

In recent years, the capacity of machine learning to improve efficiency in various fields such as pick & place operations, drone systems, manufacturing assembly, and quality control.

From the flood of sensors being integrated into robots to the climb of neural-net intervention, this robotic technology is experiencing a seismic budge in performance.

You may think how much of a place is there for machine learning in robotics? The answer can be a limited portion of developments in robotics can be credited to the uses & development of machine learning.

In this article, I am trying to collect some of the more prominent applications, along with references and links.
Before we get into the real stuff (Machine learning in robotics), let’s dig into some of the basics of robotics. You may feel it’s simple in the initial stage, but it is not as you think.

To define you about robotics, I am taking an abbreviation from SearchEnterpriseAI;
“It is a machine designed to execute one or more tasks automatically with precision and speed.”

There is a chance where some of the scientists or research debate whether a definition can be relative or depend based on the context like the concept of privacy. It might be a better approach as more & more rules and regulations are created around their utilization in varying contexts.

Another discussion added with the above whether to include robot term in innovations such as drones, autonomous vehicles, and many others.

By considering the above situation and definition given in the article, I can undoubtedly say that these various types of machines are a class of mobile robots. Robots, specially designed for a set of behaviors in a plethora of environments, their bodies & physical abilities, will replicate the best in shape for those characteristics.

There is an exception for the robots that provide medical service for humans, and possibly service robots that are meant to set up a more personal & humanized relationship.

Like many other advanced technologies today, the robotics is influenced and, in some directions, steered by machine learning.
According to the report released by the Evans Data Corporation Global Development, robotics and machine learning is the top choice for the developers for 2016, with 56.4% of participants stating that they are developing robotics apps and in which 24.7% developers are using machine learning in their projects.

The above-given overview of machine learning applications in robotics highlights five key areas where machine learning has had a significant effect on robotics at present and in coming up future.

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    Applications of ML in Robotics

    1. Assistive and medical technologies

    According to Stanford’s David L.Jaffe, an assistive robot is a device that can brain, process information, and execute actions that can help people with disabilities & seniors.

    And smart assistive technologies also exist for ordinary people or users like driver assistance tools. Movement robots give you a therapeutic or diagnostic benefit.

    Both of the technologies mentioned above are mostly restrained to labs; because they are costly for most of the hospitals in the world.

    Northwester University developed the MICO robotic arm that is the most recent example of machine learning-based robotic assistive technologies, and it is developed by combining assistive machines with more autonomy.

    The obstacles are more complicated then you imagine, even though smart assistive robots make adjustments based on the user requirements that need partial autonomy. Compared to other industries, the healthcare industry is taking advantage of machine learning methodologies and applying to robotics.


    applications of ML in Robotics


    An association through the center for automation and learning for Medical Robotics between researchers of multiple universities with a network of physicians; that has lead to the creation of smart tissue autonomous robot.
    Using innovations in autonomous learning & 3D sensing, the STAR can darn as one ‘pig intestines’ with better exactitude & reliability than the best human physicians or surgeons.

    After all this, scientists and researchers said that STAR is not a valid replacement for surgeons; but they can help to handle emergencies and majorly in performing similar types of subtle surgeries.

    1. Automatic translation

    It is an uncomplicated concept that everyone can easily understand. Machine learning can be used to translate text into another language instantaneously.

    Apart from the above, it can also be done the same thing with text on images. When it comes to the text, the algorithm can learn about how words in shape together and translate more precisely.

    When it comes to images, the neural network identifies letters from the picture, pulls them into text, and then does the translation before placing them back into the image.

    automatic translation

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    1. Computer vision
    applications of ML in Robotics


    Recommend: Computer vision applications

    Though computer vision is much related to what we are taking, there is some discussion going on is machine/robot vision is the right term when compared to computer vision because robot vision involves more than computer algorithms.

    Robot vision so much strongly linked to machine vision that it can be given credit for the emergency of an automatic inspection system and robot guidance.

    The small difference between two may be in kinematics as applied to robot vision that encompasses orientation frame calibration and a robot’s ability to affect its environment physically.

    The information available on the web has propelled advances in computer vision that, in turn has helped further machine learning-based structured prediction learning techniques universities such as Carnegie Mellon and many more.

    Now let us see a simple example that is anomaly detection with unsupervised learning like developing systems competent of discovering and assessing faults in silicon wafers with the help of convolutional neural networks like engineered by researchers at the Biomimetic Robotics & machine learning lab.

    Extrasensory technologies such as lidar, radar, and ultrasound, like those from Nvidia, are also driving the development of 360-degree vision-based systems for drones and autonomous vehicles.

    1. Imitation learning

    Imitation learning is something that is very much similar to observational learning. It is the behavior exhibited by humans do as infants and toddlers, and it comes under the category of reinforcement learning.

    It was posited that this kind of learning could be utilized in humanoid robots as far back as 1999.
    These days, Imitation learning became an integral part of field robotics industries such as agriculture, construction, military, search & security, and many more. In these types of situations, manually programming robotic solutions is much more challenging to use.

    Instead, collaborative methods such as programming by demonstration are used in conjunction with machine learning to instruct programming in the field.


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    1. Multi-agent Learning

    Multi agent Learning
    Negotiation and coordination are the significant components of multi-agent learning that involve machine learning-based robots that can acclimatize to a changing landscape of other agents/robots and find equilibrium strategies.

    Examples of multi-agent learning :

    • No-regret learning tools.
    • Market-based distributed control systems

    Robots combined to build a better and more inclusive learning model than could be done with a single robot depending on the concept of exploring a building, its room layouts, and autonomously edifice a knowledge base.
    Each robot will develop its catalog them combines with other robot’s information/data sets; the distributed algorithm outperformed the standard algorithm in creating this knowledge foundation.

    This type of machine learning approach enables robots to compare datasets or catalogs, reinforce mutual observations & correct omissions. And undoubtedly, it will play a near-future role in several robotic applications, including airborne vehicles and multiple autonomous lands.

    • Conclusion

    Most of the organizations around the globe are using USM AI opportunity landscape research to gain data-backed confidence in their AI plannings. And they use it to select AI initiatives most likely to deliver a return on investment.
    From natural language processing to computer vision & from robotics to robotic process automation, how you can tie together AI to gain a spirited advantage.

    If you are also planning to take advantage of machine learning in robotics for your organization, contact us.
    Our USM AI professionals will help you in giving complete information.

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